Representing and using legal knowledge in integrated decision support systems: DataLex WorkStations
Graham Greenleaf, Andrew Mowbray and Peter van Dijk
Published in Artificial Intelligence and Law   Kluwer, Vol 3, Nos 1-2, 1995, 97-124; Published on the web as part of the authors' web pages.
Graham Greenleaf	
Associate Professor of Law, University of New South Wales Sydney 2052 Australia; graham@austlii.edu.au
Andrew Mowbray
Senior Lecturer in Law and Director of Undergraduate Programmes, University of Technology, Sydney, Broadway 2007; andrew@daustlii.edu.au
Peter van Dijk
Lecturer in Law (P/T), University of Technology, Sydney, Broadway 2007
Abstract: There is more to legal knowledge representation than knowledge-bases. It is valuable to look at legal knowledge representation and its implementation across the entire domain of 'computerisation of law', rather than focussing on sub-domains such as 'legal expert systems'. The DataLex WorkStation software and applications developed using it are used to provide examples. Effective integration of inferencing, hypertext and text retrieval can overcome some of the limitations of these current paradigms of legal computerisation which are apparent when they are used on a 'stand-alone' basis. Effective integration of inferencing systems is facilitated by use of a (quasi) natural language knowledge representation, and the benefits of isomorphism are enhanced. These advantages of integration apply to all forms of inferencing, including document generation and case-based inferencing. Some principles for development of integrated legal decision support systems are proposed.
Key words: integrated, natural language, hypertext, document assembly
Contents
1.	'Integrated' computerisation of law
1.1.	Meanings of 'integration'
1.2.	DataLex WorkStations
2.	Hypertext and text retrieval engines
2.1.	The hypertext engine
2.2.	The text retrieval engine
3.	Integrating hypertext and text retrieval
3.1.	From searching to browsing
3.2.	Browsing to searching
3.3.	Searching for smarter searches
4.	The inference engine
4.1.	Rule-based inferencing
4.2.	The  knowledge representation: (quasi-) Natural language
5.	Integrating inferencing with hypertext and text retrieval
5.1.	The limits of inferencing
5.2.	Explanation facilities in the Workstation
5.3.	Distributing access to the knowledge base
5.4.	User Control over inferencing - a reason for integration
5.5.	Isomorphism and The value of 'English-like' representations
5.6.	Isomorphism and Developer control of inferencing
6.	Multi-modal inferencing and integration
6.1.	Document Generation and the inference engine
6.2.	'Examples' and the inference engine
7.	Future work on integration
7.1.	Exploiting common elements in different representations
7.2.	Terminology and theory for integration
Acknowledgments
References

1.	'Integrated' computerisation of law
There is more to legal knowledge representation than knowledge-bases, just as there is obviously more to the computerisation of law than legal inferencing systems. It is valuable to look at legal knowledge representation and its implementation across the entire domain of 'computerisation of law', rather than focussing on sub-domains such as 'legal expert systems'.
[Paquin et al 1991] take a similar approach:
We think that a shift from a dominant mono-technological point of view, that is, a legal expert system, to an integrated point of view where the user is included in the same way as several technologies, is more productive in terms of a real world scale system. This point of view relinquishes the pretensions of automated legal decision-making. It considers those systems for what they are: information systems that help human decision-making.
The perspective from which our work derives is that of a developer of legal computer applications intended to be of immediate practical utility. One emphasis of our work is therefore on how to make the best use of current technologies. From this perspective, we first argue that  effective integration can overcome some of the limitations of the current paradigms of legal computerisation which are apparent when they are used on a 'stand-alone' basis. The use of integrated tools in the building of ‘real world’ applications can save application developers from attempting to use the techniques of one mode of computerisation for purposes for which it is not suited. It is sometimes difficult to anticipate at the outset of a project what combination of tools will be needed. Attempting to force square pegs into round holes is rarely satisfactory. 
Second, we argue that effective integration of inferencing systems is facilitated by use of a (quasi) natural language knowledge representation, and that this applies to all forms of inferencing. The advantages of such an approach for isomorphic knowledge representation is argued. Third, we argue that a theoretical foundation  for development of integrated legal decision support systems needs to be developed, and we propose some basic principles for discussion.
In the course of these arguments we discuss ways in which legal domain knowledge can be represented by application developers for use by the text retrieval and hypertext components of systems. 
Paradigms of computerisation of law
Computerisation of law has developed from a number of originally unrelated technologies: the development of online free text retrieval systems from the 1960s; the revival of artificial intelligence research in the form of expert systems in the 1970s, the development of automated document generators, and the ‘rediscovery’ of hypertext in the late 1980s. Lawyers are interested in the computerisation of a number of different aspects of legal practice, including retrieval of documents relevant to decision-making, other forms of research, the decision-making itself, the generation of legal documents, litigation support and computer-assisted learning.  We use ‘computerisation of law’ to encompass both the computerisation of these various aspects of legal practice, and of the legal source materials (such as cases, statutes, commentary) used in them.
Most commercially published legal applications have implemented only one of the four dominant paradigms.  Text retrieval systems have, of course, been overwhelmingly dominant. Legal hypertext systems have become increasingly common in the last few years, and have usually been integrated with text retrieved but often only to a limited extent. There are still very few legal inferencing systems in commercial use, and their integration with hypertext or text retrieval is still exceptional. Automated document generators have been commercially available for over a decade, but only on a relatively small scale and with virtually no integration with other technologies.  This lack of integration is not peculiar to law, but has been observed to be a general feature of the computerisation of information [Frei (1990)]. There are an increasing number of exceptions in commercial computerised legal systems, some of which are mentioned in this paper.
Each of the current paradigms have prompted theoretical legal and computing research, ranging from considerable research on such matters as the jurisprudential models implicit in various types of legal expert systems, and the adequacy of Boolean retrieval for legal research, to a far more limited amount of research on the principles of hypertext systems suited to law or legal document generators. Research on the relationships between these different approaches has been more sporadic., but includes substantial  research on the use of AI techniques in information retrieval, and work on the development of integrated computerised workstations for public administration. In the last few years it has become increasingly common for articles on legal computerisation to recognise the value of using hypertext in conjunction with other paradigms of legal computerisation. Vandenberghe stressed the importance of integration over a decade ago ([Vandenberghe, 1982]; see also [Koers, 1990]), and others have occasionally done so since [eg [Oskamp and van der Berg, 1990], [Paquin et al, 1991], [Greenleaf, Mowbray and Tyree, 1991], [Mead and Johnson, 1991], [Soper and Bench-Capon, 1994]).  However, it is still true to say that there has been  relatively little development of any general theory exploring  a comprehensive approach to the integrated computerisation of law.
Limits of the paradigms as 'stand-alone' technologies
One of the main justifications for exploring the integration of these technologies is that each has well-recognised limitations and deficiencies when it is used on a 'stand-alone' basis for the computerisation of law. We will mention a few. Most users have significant problems in formulating effective queries in text retrieval systems (at least those based on boolean connectors and proximity), particularly where any legal concepts must be represented by the user in queries. With text retrieval alone, it is often difficult to go to neighbouring text items which are not in the retrieved list, in order to understand the context of a retrieval item. Hypertext systems require that all associations between text items be identified in advance by the system developer, whereas text retrieval systems at least allow some users to retrieve items which have unusual and unanticipated connections. Inferencing systems tend to be very brittle because of the endemic open textured nature of legal language, and it is difficult to build and maintain adequate explanatory mechanisms which assist the user to resolve these problems, or explain system outcomes. Differences between each of the paradigm technologies in terms of noise and silence (relevance and recall), and in costs of development in relation to coverage, are suggested in [Paquin et al, 1991] at 2.2.
1.1.	Meanings of 'integration'
'Integration' has at least two distinct meanings in relation to legal computerisation. It refers primarily to the integration of technologies with inherently different functions (as we have used it above), and with consequently different representations of legal information and knowledge. Each software ‘engine’ requires its own form of representation of the legal ‘knowledge’ and ‘data’ which it manipulates: a rule-base (or other form of knowledge-base) for an inference engine;  a concordance for conventional text retrieval; and a hypernet (network of nodes and links) for hypertext. For some software, these are combined in a common text file, but separate intermediate binary files are created to enable each of the respective engines to function (either permanently on disk or temporarily during run time), so there are separate knowledge representations for each engine.
Whatever engines are used, there is potential to exploit all the different possible forms of integration between them to achieve useful results. Figure 1 shows, at the broadest level, the six potential directions of integration where three conventional engines and representations are used, and Table 1 gives examples of each of these ('rule-base to hypernet' etc).
Other engines and their corresponding representations would open up different possibilities for integration. Simple examples are the representation of documents as graphic images, and other legal information as video or as interactive graphics, all of which are very important for litigation support (see [Greenleaf and Mowbray, 1993b], Chapter 5), and may well become more important in the research systems which are the main focus of this paper. More complex search engines, using representations in addition to a conventional concordance, such as inference networks [Turtle and Croft, 1991], would open up quite different possibilities.

Figure 1:  Six potential  directions of integration between three representations

Figure 2: Six potential forms of interaction within a knowledge-base

'Integration' can also refer to the interaction of  different forms of legal inferencing. In discussions on the combining of case-based reasoning and rule-based reasoning, [Rissland and Skalak, 1989] refer to 'a mixed paradigm approach to reasoning' and [Zeleznikow, Vossos and Hunter, 1994] refer to 'multi-modal reasoning'. Document generation, a third form of legal inferencing that differs not only in its outcomes (documents rather than conclusions) but also in that it is usually based on inferencing by decision networks or some similarly procedural approach, may also benefit from interaction with rule-based and case-based inferencing. These potential interactions are represented in Figure 2.
A knowledge base can therefore include different representations of legal knowledge, such as a rule-base, a case-base and document templates. It should be valuable to ask whether there are common methods of representation which will make interaction between these various forms easier to achieve, and will also facilitate the interaction of each of them (ie the knowledge-base as a whole) with other engines and representations (hypertext, text retrieval etc).
1.1.1.	Legal knowledge,  data and representations
Each type of representation discussed above is conceptually distinct from the legal texts which are usually one of its principal sources (see [Bing, 1991], [Susskind, 1987], [Poulin et al, 1993] for valuable discussions). The expression ‘knowledge representation’ is most often used in relation to inference systems, indicating that a knowledge-base involves more than mere data. Concordances and hypernets are more easily seen merely as ‘data’, but the creation of text retrieval and hypertext systems necessarily involves some addition of legal knowledge to the ‘raw’ textual sources of the law which can range from the trivial to an embodiment of legal knowledge of complexity comparable to the creation of a knowledge-base. 
A list of related concepts, for example, is as of much relevance to an inference engine (where it may allow assignment of values of attributes) as it is to a hypertext engine (where it can be used as a cross-reference, or an alias) or a free-text retrieval system (where it can be used to expand search terms). Other examples are given throughout this paper, and possibilities of exploiting common elements in different representations are discussed. It is therefore very problematic to distinguish inferencing systems from hypertext or text retrieval on the basis of a distinction between ‘knowledge’ and ‘data’ representation. It is more useful to say that any integrated application involves various ‘information representations’. 
1.2.	DataLex WorkStations
The DataLex Workstation Software and various applications we have developed using it will be principal means of illustrating the arguments sketched above. This article will also serve as a more comprehensive explanation of our approach than has previously been published.
1.2.1.	DataLex Workstation software
The DataLex Workstation Software integrates inferencing (rule-based backward and forward chaining, document generation, and a limited form of case-based inferencing), hypertext and (boolean/proximity) text retrieval into a general-purpose tool for the representation and processing of legal  information. The software is a shell which has been used to create applications in a variety of legal domains.  
From a user's perspective, some of the advantages or features of the use of a WorkStation application in a particular legal domain include the following: ability to move easily between inferencing, hypertext browsing and text retrieval, using whichever tool is best suited to a particular part of the user's research task; use of automated, context-based searches to reduce the need to master boolean search languages; ease of access to inferencing dialogues when needed, through the hypertext acting as a distributed front end; and inferencing dialogues and explanations which provide hypertext 'exits' to open-ended textual materials which can assist the user to interpret what the inferencing system is explaining or asking. A user can collect text generated by any of these components so as to incorporate it into reports with the degree of detail that she requires.  
Andrew Mowbray wrote the DataLex Workstation Software in 'C', and continues to develop it. It comprises three distinct engines which share a common interface: a free-text retrieval engine (AIRS), a hypertext engine (HYPE) and an inferencing engine (YSH).  These engines derive from a number of earlier programs by Andrew Mowbray, integrated into the Workstation software since 1991 [Greenleaf, Mowbray and Tyree, 1991]. The software's user features are documented in [Greenleaf, Mowbray and van Dijk, 1994a] and developer features in [Greenleaf, Mowbray and van Dijk, 1994b].  It runs under Microsoft Windows, Apple Macintosh, Unix and MS-DOS. Examples in this paper are from the Windows and Macintosh versions. 
Applications are written using text editors. We have developed a number of Unix-based text processing tools (distinct from the DataLex Workstation Software)  to automate the mark-up of legal texts for hypertext and text retrieval by the DataLex software.
We do not consider any of the three engines on which the Workstation software is based to be complete, or necessarily the best form of its type of engine. Each could be usefully enhanced, and proposed or possible enhancements are discussed below where each engine is described. It is possible that  a  text retrieval engine fundamentally different from our convention concordance-based approach could be preferred, and likewise with the inferencing techniques we have implemented. We would not expect that such developments would change our main argument concerning the value of a systematic approach to integration, or reduce the value of many of the illustrations we give. Enhanced or different engines simply create the potential for new and better forms of integration. The DataLex WorkStations approach is put forward only as one example of a valuable approach to integrated computerisation of law.
We would also be surprised to see two aspects of our approach made redundant by future developments: the centrality of hypertext in any systems where legal interpretation is important; and the value of some form of (quasi) natural language knowledge representation in inferencing systems.
1.2.2.	The Workstation interface
The main design criteria of the Workstation software's interface are simplicity and a desire to keep the user oriented by the 'background' presence of the text that they last accessed, irrespective of what 'foreground' task they are currently undertaking. The screen is usually presented as a full screen of text, with only a menu bar and the title of the document current displayed occupying any other part of the screen. Dialogue boxes or list or result windows overlap this 'background text' only when required (see Figure 3). Any other on-screen 'buttons' or other items which do not appear in the original legal text are kept to a minimum. The interface only allows a single text window to be open, with access to previously accessed documents being via backtracking and the history list. We consider that this provides better user orientation than multiple overlapping text windows.

Figure 3: The WorkStation interface - A text screen with an overlapping results window
1.2.3.	Workstation applications
 The Workstation software has been used to develop several commercial and academic applications, and has been used extensively for teaching purposes.  The most developed applications, to which reference will be made throughout this paper for examples, are:
• 	The Intellectual Property Workstation  [van Dijk,  Mowbray, Greenleaf, and Griffith, 1993- ]: over 25MB of text including legislation, explanatory materials, headnotes to the Intellectual Property Reports (Butterworths); it includes more than 150,000 links to 30,000 labels, 150 rules, 11, 500 'noteups', and 1,500 cross-reference lists.
• 	The Australian and New Zealand Privacy Workstation  [van Dijk, Greenleaf, Mowbray and Gunning, 1991- ]: over 7MB of text including legislation, guidelines, a monthly newsletter (IBC); includes over 40,000 links to over 5,000 labels and over 200 rules.
• 	The Australian Insurance Law Workstation [van Dijk, 1994]: over 8MB text, including legislation, back set of a monthly newsletter, and a loose-leaf service; over 52,000 links to 10,000 labels.
1.2.4.	A systematic attempt to explore integration
The most unusual feature of the DataLex Workstation software is the variety of features that it provides to application developers to explore various methods of integrating hypertext, inferencing and text retrieval. In the WorkStations that we have developed, we have attempted to explore as many uses as we can of the potential forms of integration that the software allows. Examples of these forms of integration are discussed throughout this paper. Table 1 summarises the potential forms of integration provided by three engines such as those in the WorkStation software, the forms that have been implemented in the Software, and gives examples and references to later parts of this paper where they are discussed.
As [Soper and Bench-Capon, 1994] have noted (referring only to integration between inferencing and hypertext):
'... what is offered is not simply a means of one technology enriching another ... but a set of new possibilities where the synergy between the two technologies can lead to new kinds of interaction, and hence give rise to entirely new styles of application.'
Table 1: Examples of integration  between different engines
	Type of integration between engines	Examples	See	
1	Hypernet to knowledge-base	browse –> infer		
Y

Y	Distributed and context-dependent interface to inferencing system	•evaluation of goals from links in cross-reference index
• evaluation of goals from links embedded in  text	5.3	
Y	User access to multiple inferencing sessions (complete and incomplete)	• inferencing sessions (current and past) appear in history list / backtrack path	5.3	
2 	Knowledge-base to hypernet 	infer –> browse		
Y	Hypertext terms in inferencing dialogues to assist user resolution of open textured predicates ('open-ended’)	• links from prompts	5.2.1	
Y	Hypertext terms in  terse inferencing system explanations to augment them (also 'open-ended’)	• links from 'Why' and 'How' explanations and Reports	5.2	
Y	Access from expert system to hypertext of rule being evaluated	•  ‘Rule’ option	5.2.5	
	Hypertext jump triggered by inferences			
3 	Concordance to hypernet 	search –> browse		
Y	Hypertext display of search results		3.1	
Y	Displays of search term contexts added to history list / backtrack path		2.1, 3.1	
Y	Searches for nodes indexing automated searches	• searchable synonyms	3.3	
4  	Hypernet to concordance	browse -> search		
Y	User-controlled searches at any time			
Y	Pre-stored search triggered by hypertext link  	• 'Noteup' buttons
• 'self-updating indexes'	3.2	
Y	User modification of pre-stored searches 	• 'leveraged' searches	3.2	
Y	Searches  (current and past) appear in history list / backtrack path		2.1, 3.1	
5 	Knowledge-base to concordance  	infer–> search		
Y	User-controlled searches at any time			
	Searches triggered by inferencing evaluations	• find texts more recent than knowledge-base 	3.2	
	Pre-stored searches using inferencing predicates as search terms		3.2	
6 	Concordance to knowledge-base 	search –> infer		
Y	Searches retrieve rules (inference commences from goal in cross-reference index to rule - as in 1 above)	 	5.3	
Y = implemented in DataLex Workstation software
2.	Hypertext and text retrieval engines
Before exploring the considerable potential for exploiting integration between hypertext and text retrieval (see the next section), we summarise the operation of these engines in the Workstation.
2.1.	The hypertext engine
Legal materials are particularly suited to hypertext presentation. Legal texts are densely cross-related, either expressly or by implication: cases interpret statutes; cases cite cases; definitions define terms used elsewhere in the same statute; regulations have as their source the provisions of a statute, and legal commentaries are usually a thicket of cross-references. It is likely that the hypertext techniques and styles appropriate to law will be particular, at least to some extent. For example, the importance of authoritative definitions (as are provided in statutes)  is unlikely to be matched in other domains. A variety of hypertext applications to law have been reported, and hypertext is often now mentioned in relation to legal inferencing systems. Hypertext appropriate to law has yet to be defined fully, but there is now a considerable literature on the general features of hypertext and the issues involved in choice of techniques and styles. The explosion of world-wide use of hypertext since 1994 because of the growth of the world-wide-web, and the relative consistency of features in www browsers such as Mosaic, Lynx, Netscape and Cello means that standard hypertext features (and user expectations) are emerging because of the internet.
Hypertext is implemented in the Workstation software in a way which is intended to be intuitive to users. The main features of the Windows version of the hypertext engine, from the user's perspective, are as follows and appear under the 'Browse' menu (see Figure 4).  Text which can activate a hypertext link appears in a user-configurable format (such as italics or  bold or different background or foreground colours). Links are activated by mouse clicks. If link text is designated as having more that one possible destination node, a cross-reference window appears to give the user a choice of destinations.

Figure 4: The Browse Menu (Intellectual Property Workstation)
Text is continuous, rather than discontinuous nodes based on a fixed size screen or ‘card'. This allows a user to regard the text as being similar to that contained in a book, by scrolling backwards and forwards through the (screen) pages. However, scrolling is limited to developer-designated 'blocks' of text (eg a whole Act, a single case), so that users cannot accidentally 'scroll out' into irrelevant material. Scroll boxes automatically adjust to the proportions of the current block. To enhance orientation, the title of the first node in the block (eg the title of an Act) always appears on the screen's title bar.
Access to any particularly significant node, such as tables of contents or indices, is via a developer-designated Landmarks list on the Browse Menu - the electronic equivalent of the coloured plastic tabs in a loose-leaf service.
Backtracking a single node (or sequentially through multiple nodes) can be done in a number of ways, most intuitively by simply clicking the right mouse button (ie left = forward, right = back). 
A History List of all nodes navigated by the user can be selected from the Browse menu. The History List also lists any searches that the user has previously done (see Figure 5), and any consultations previously commenced. Any item on the list may be selected to allow quick return to any node in the backtrack path (hypertext), any previous search results (retrieval), and to resume any previous consultation (inferencing). 

Figure 5: History List showing searches
The Workstation software supports in-line graphics, and a whole graphic can constitute a link. Links activating computerised video are also supported, with the video appearing in a superimposed window in a Workstation. The Media Law Workstation [Mowbray and van Dijk, 1994] makes extensive use of integration between legal texts and video.
We intend to add to the hypertext engine such relatively standard features as user-initiated links, bookmarks and annotations. 
2.1.1.	Automated hypertext creation
Creation and maintenance of hypertext links in large and complex bodies of text is very difficult. This is particularly so where text undergoes regular change, as is the case with legal commentary, and to a lesser extent with statutes and regulations. If hypertext links are inserted in source documents manually,  large or complex hypertext systems become impractical. As detailed in [1..2.3] above, the WorkStations we have developed involve many thousands of hypertext links, the majority of which are 'lateral' links (eg to definitions, to cross-references between sections or between cases, or to references to sections in cases ) rather than 'hierarchical' links (eg tables of contents, footnotes). The creation of such 'lateral' links is complex as the link text must usually be recognised in the body of the anchor node, and will often occur in non-standard forms. In contrast, it is relatively easy to create 'hierarchical'  links  by relying on heading levels and numbering in source documents, and (if needed) sequencing links (eg 'next', 'previous').
To eliminate manual marking up we create automated marking-up scripts for each category of document which has a reasonably regular form (statutes, regulations, cases, commentary etc). These standard ‘templates’ can be used to  create useful hypertext automatically with almost all of the desired functionality. The DataLex markup language differs from SGML (Standard Generalised Markup Language) but is readily convertible to it, and has been converted to HTML (hypertext mark-up language) for the purpose of constructing content for a World-Wide-Web server. The details of our automation techniques are as yet unpublished, but will be documented in future work. There is relatively little published work on the automated creation of legal hypertexts: see [Choquette, Poulin and Bratley, 1995] and work cited therein.
However, most documents have non-standard features. For example, while most statutes from common law jurisdictions have explicit interpretation sections in which defined terms are easily recognised (eg "..." means "..."), other sections may implicitly define a term by a wide variety of non-standard means (eg ‘In this Act, a .... will be taken to be a ...’). These non-standard definitions must be identified and designated as destination nodes, and the corresponding link texts identified and located, to create comprehensive and sophisticated hypertext. Non-standard structural features of documents must also be identified. The Privacy Act 1988 (Cth), for example, provides that each of the eleven Information Privacy Principles (IPPs) contained in s14 shall be interpreted as if it were a separate section (s2). Useful hypertext must treat each IPP as a separate node. 
We deal with non-standard structures by modifying the standard marking-up script so that it recognises the non-standard features of a particular document. So, if a document is important enough, it will have its own marking-up script. The alternative is to edit the source texts after the standard automated marking up has been done, but this will be ineffective if the source text is amended. This method allows all source documents to remain in the raw text form, so the system can be re-created automatically by use of the script files, whether or not some source texts have since been amended.
The creation of such 'enhanced' hypertexts usually requires the attention of a legal domain expert, and the embodiment of the expert's knowledge of the legal text's semantics and structure in re-usable mark-up scripts is a form of knowledge representation.
2.1.2.	The cross-reference index
A user who is browsing, say, a section of an Act, should be able to access other textual sources  related to that section in an intuitive way.  In Australia these include delegated legislation, Parliamentary Explanatory Memoranda, case reports, and academic commentary. Many desired links between these sets of documents may be able to be identified automatically from the wording of one of the texts. For example, regulations tend to cite the section under which they are made in a relatively unambiguous fashion (whereas sections of statutes do not cite any regulations which may be made under them).  Such links should be inserted automatically in the source text, as described above.
However, in many cases where multiple related sources are involved, it will be impossible to identify in any automated way their precise correlation with other sources in the set. For example, legislation often ends up with different clause numbering than that the Explanatory Memoranda and Bill preceding it, due to amendments. Correlations between sections of old and new Acts (for example, Australia's Patents Act 1952 and Patents Act 1990) may be vital in providing a link between the new Act and old case-law, but it isn't apparent from the face of the legislation. In these situations, the creation of cross-references is equivalent to intellectual indexing of hypertext nodes. We refer to these as 'non-inherent' links.
A third possibility is where it is possible to automatically recognise that there should be a link between two nodes, but the source text of one of the nodes does contain explicit link text (eg a Regulation may identify the section under which it is made, but there is no text in the section which gives an intuitive link to the regulation). It is undesirable to add extraneous link text to the original source documents.
Where a hypertext application is based primarily around a statute or other identifiable 'key document' which is related to a multiple other document types, so that there are numerous express links and implied links (ie the second and third types above) between documents in the set, the best way to present the implied links is problematic.   If we attempt to provide direct links from each of these sources to most other  sources (many-to-many links), the resulting hypertext resembles a bowl of spaghetti (‘...in a sense hypertext gives us a goto, and a goto, as we all know, produces spaghetti...' : [van Dam, 1988] ). Insertion of link text to multiple related sources in the source texts is undesirable  and maintenance will be very difficult. 

Figure 6: Hyper-spaghetti

Figure 7: Avoiding hyper-spaghetti

An alternative approach that we have found useful where a hypertext application is based primarily around a statute of other identifiable 'key document', is to eliminate implied many-to-many links by routing  links through a single shared ‘cross reference index’ for the set of documents. Where there are cross-references to a node, a 'X-REF' button appears to the left of the node title. In the Privacy Workstation (see Figure 8) it contains, for each section of any important Act, a list of links to all other source documents relevant to that section. In DiskROM Australia's Corporations Law CD-ROM, a similar device is called an ACTRIX. The cross-reference index only works well where nodes in the sources are usually related to only one node in the 'key document'.

Figure 8: The Cross-Reference Index (Privacy Workstation)

2.2.	The text retrieval engine
The text retrieval engine is a conventional free text retrieval system based on a concordance and using boolean and proximity searching. It creates and uses a five level concordance: chapter, article, section, paragraph and word. Plurals and possessives can be (optionally) concorded as being equivalent to their singular forms. Synonyms for specific words and phrases may also be defined in synonym lists, allowing all synonyms of a search term to be retrieved. The retrieval functions are ‘exactness', rather than ‘nearness’, in the sense that retrieved documents are not ranked by  some measure of likely relevance to the search request [Bing, 1987]. 
The user interface is a very simple search window. Search terms may be connected by boolean connectors and proximity connectors (including within designated fields or segments). A search term commencing with a boolean connector is treated as a search modification and appended to the previous search request (see below for other aspects of search modifications). Right hand truncation is allowed (* suffix), as is a user specification that synonyms are to be searched for (& suffix). 
The scope of searches may be limited by a ‘search area’ option which presents a list of defined subsets of the database which may then be selected or de-selected. The current search is then automatically re-executed over the new search area.
The text retrieval engine would benefit, at least for some applications, from the addition of some form of relevance ranking of retrieved articles. A more ‘interventionist’ search interface [Bing, 1986] is also desirable. 
3.	Integrating hypertext and text retrieval
3.1.	From searching to browsing 
In WorkStations, search results are displayed as hypertext. When a search is executed, the list of titles of retrieved articles (the ‘retrieved list’) is displayed (Figure 9). Selection of an article from the list takes the user to the hypertext node at the commencement of that article. From that point the user can use hypertext browsing techniques to follow any other associations  that a search result suggests. In that sense, the display of a retrieved article is open-ended. 
The retrieved list is also treated as if it were a hypertext node, although it has been created dynamically by the search. The various methods of backtracking will take the user back to the retrieved list display.  As an extension of this, all searches are included in the History List (see Figure 5). When a search is selected from the History List, the retrieved list is displayed. If the user then activates the search window, the previous search is displayed and can be modified. 
Search terms can be displayed in the context in which they occur in retrieved articles by use of the Scan command (Search menu or F7), successful use of which takes the user to those point in the retrieved articles where search terms occur. Search terms appear in user-configurable format (colours, italics etc), and where a search term is also hypertext link text, a 'blend' of the configurations chosen for each will be displayed. Each instance of a context display can be returned to by backtracking, and will appear on the History List as a separate entry.

Figure 9: A retrieved list (Intellectual Property WorkStation)
3.1.1.	Displaying search results - the tip of an iceberg?
Since the articles in a retrieved list are linked to numerous other nodes not in the retrieved list, a retrieved list which is part of a hypernet can be seen as 'the tip of an iceberg' of nodes - first there are nodes linked directly to those in the retrieved list, then those linked to those nodes, and so on.  Since the existence of a link implies some semantic relationship between the two nodes linked, it seems likely that nodes linked directly to those in a retrieved list would have some greater than average relevance to the purposes of the search that created the retrieved list (items in the cross-reference list are a good example). If this is so, then hypertext display of retrieved articles can be seen (informally) as providing greater recall than only the items in the retrieved list, but without sacrificing precision because there is a rough-and-ready measure of relevance (the distinction between those items in the retrieved list, those linked directly to those items, and those only linked indirectly). How does the user find which nodes in the 'iceberg' are relevant to the search purpose? We have merely left it to the user to decide from inspection of the retrieved node text which links from retrieved list nodes seem most promising to explore, and have no empirical evidence of the extent (if any) to which retrieval performance is improved. [Savoy, 1994] suggests a number of ways by which hypertext links can be used to improve search performance.
3.2.	Browsing to searching 
It is well known that many users of text retrieval systems have difficulty in formulating  search queries which are both precise and comprehensive  [Bing, 1987], and some have gone so far as to suggest that conventional free-text retrieval systems (ie those retrieving only exact matches with search queries using boolean and proximity connectors) place impossible demands on users [Blair and Maron, 1985]. Many alternatives to the conventional model have been proposed, often based on different methods of relevance ranking, and are the subject of considerable debate (see [Savoy, 1994] for a recent review). 
We suggest that hypertext can be integrated with text retrieval to provide another way to reduce user difficulties with text retrieval. It may be as relevant to retrieval methods based on relevance ranking as it is to conventional retrieval. If we assume that the developers of a legal information system know far more about its structure and contents, and effective ways of searching it, than most users are ever likely to know, then it should be valuable to explore means by which that 'domain knowledge' can be embedded in the system in a way that users can utilise. A hypertext system gives an ideal opportunity to do so, because users will often browse to a key document that is relevant to their overall problem (for example, a section or a case), at which point they decide 'I need to know more about that'. However, many users will not have sufficient understanding of connectors, truncation or use of synonyms to be able to carry out a precise and comprehensive search even when they are trying to search for such non-conceptual items. However, in navigating to the node in which they are primarily interested, they have provided the system with sufficient contextual information to construct such a search for them.
The Workstation software allows hypertext links to trigger stored searches. We have made extensive use of this in the WorkStations we have developed. For example, every legislative section in any of the WorkStations has a 'Noteup' button near the section title (see Figure 10), which triggers a stored search using synonyms (for the various forms in which section numbers might appear) and two proximity connectors. Similarly, in the Intellectual Property Workstation, every reference to a case has a 'Noteup' button next to it, which triggers a search for the citation for the case and alternative citations (using a synonym list).  As a result, without ever thinking about searching, users are constantly executing searches which would be beyond the sophistication of most of them. These stored searches are created and inserted automatically by marking-up scripts - another form of knowledge representation.

Figure 10:  'Noteup' for Copyright Act 1968  s74 (Intellectual Property Workstation)
Another use for stored searches is as a self-updating index. Conceptual indexes provide one of the most useful retrieval tools in paper-based legal products. However, one of the problems of any body of changing legal text is that the text is likely to change (new cases, legislation, commentary) faster than the periodic (and expensive) production of new intellectual indexes. Without claiming that a stored search could produce as valuable a result as intellectual indexing, it may still be very useful to have an 'Update' button attached to each index entry, triggering a search for items entered into the system since the last update to the index, and based on the conceptual categories used by the indexer, augmented by appropriate truncation and use of automated synonyms.
By relying on these 'context sensitive stored searches' users could use a Workstation primarily for text retrieval without ever using the search interface or knowing how to do searches. Informal discussions with users indicate that this is in fact what many of them do.
More sophisticated users can modify pre-stored searches to make them more precise, thereby 'leveraging' the search knowledge already stored in the system. For example, a user wanting to find information in the Intellectual Property Workstation on non-use of trade marks by registered users might choose to start by using the 'Noteup' to s23 of the Trade Marks Act 1955 (the 'non-use' section) which retrieves 59 cases, and then simply amending the stored search by the addition of  'and registered user*', which reduces the retrieved list to two articles.
Inferencing and stored searches
A further use of stored searches could be to check, at the conclusion of an inferencing session, whether there were relevant texts in the system which were of a later date than the last update of the knowledge-base, given that knowledge-bases are likely to be updated less frequently than source texts. This could be implemented by a hypertext link or (with modification of the WorkStation software) by the inferencing software. 
One possible approach to constructing automated searches to do such updating would be to utilise the predicates and logical operators used in a particular rule in the knowledge-base to construct a search. Natural language predicates are similar to index terms (whether they derive directly from the source texts or are designated by domain experts), and AND and OR logical operators have direct analogies to boolean connectors, so it may be possible to use a rule-base to construct self-updating searches. Integration between inferencing and searching is the least developed aspect of our WorkStations as yet.
3.3.	Searching for smarter searches
Once a Workstation contains stored searches, it is desirable to assist users to find them. They may find the searches by browsing provide they are placed in a sensible context (as with the 'Noteup' buttons described above). However, they will also find them if they do a search that retrieves a section or a case, because these sections and cases will come with a 'Noteup'. Another example is the where search results include an item from a self-updating index., as described above.
A more self-conscious implementation of this feature is to include the synonym list for a Workstation as part of the searchable text files, possibly with each group of synonyms as a separate article. If a user does a search which includes a term found in a synonym list (or lists), then one of the items they will retrieve is the synonym list(s) for that term. It is a simple matter to attach to each list a hypertext link which will then execute a search for the synonyms, but it may be just as useful in many cases to have found a reminder that synonyms should have been used in the original search, so as to then amend it.
In all these cases, searches have found hypertext nodes which (in effect) act as an index to a further (stored) search. 
4.	The inference engine
Rule-based inferencing is discussed in this and the next Part, but the inference engine used by the DataLex Workstation software also supports rule-based  document generation and  a limited form of case-based inferencing, using rules which have a similar syntax. These other forms of inferencing are discussed in Part 6 below. 
4.1.	Rule-based inferencing
By default, rules are both backward and forward chaining. The inferencing mechanism works by evaluating a rule which has been selected by the user (either from a list of goals, or triggered by a hypertext link). Inferencing proceeds by the execution of statements comprising a rule. Backward chaining is used to determine unknown facts. When a fact value is inferred or supplied by the user, evaluation of all rules which contain that fact in their conditions takes place by forward chaining. 
Rules may be declared to be of  types FORWARD, DAEMON, PROCEDURE, LISTED or GOAL, by using one or more of these keywords at the start of a rule. The uses of these rule types is explained in Parts 5.4 and 5.6. A FORWARD rule  will ask for user intervention (if necessary) to provide the unknown values of conditions, whereas a DAEMON  operates ‘silently’, failing rather than asking for user intervention. A PROCEDURE must either be explicitly called by another rule or explicitly invoked by the user. 
Any rule may be declared to be subject to two rule qualifiers.  LISTED rules have their titles displayed in a system's menu of goals, to allow one form of user selection. GOAL rules are not automatically evaluated when invoked by backward chaining — user confirmation is required before inferencing proceeds. GOAL rules are, by default, also LISTED, and  are intended to be those rules appropriate to commence a significant separate inferencing process. 
4.2.	The  knowledge representation: (quasi-) Natural language
We call the inferencing engine's knowledge representation ‘(quasi) natural language’ for two reasons. At least where statutory materials are concerned, it often approximates a paraphrase of the legislation even though it is a formalism. Secondly, it permits re-parsing of these English-like rules to generate inferencing dialogues. Similar approaches have been taken by Waterman by Johnson, Mead and Dayal  and (to a lesser extent) by [deBessonet, 1991]. It is misleading to call such a representation 'natural language', because it is at best a paraphrase according to strict rules, so we will alternate between the awkward '(quasi-) natural language'  and the more convenient but language-specific 'English-like' for want of a better alternative.
4.2.1.	Facts
All dynamic information is stored as a set of facts. Facts are referred to by a descriptive phrase or sentence. Most facts are boolean and may have as values 'true', 'false' or 'unknown'. The name of this type of fact should be composed of a subject, then a verb (expressed in the positive or negative) and, optionally, an object. For example:
	the intestate satisfies s23(1)
	the work is "original"
	section 9 applies

Provided that boolean fact names appear in this form, the inference engine will normally be able to automatically affect sensible translations during problem sessions, by use of a set of parsing heuristics. It is occasionally necessary to declare the forms of a verb. For the first example above, the following automatic prompts and translations would be generated:
	Does the intestate satisfy s23(1)?
	The intestate satisfies s23(1).
	The intestate does not satisfy s23(1).

Non-boolean facts can have values which are numbers, amounts, dates or genders. The inference engine will normally recognise when a fact is non-boolean, and give it its correct type, from the context in which it occurs in a rule. Types can be declared if automatic recognition fails. Non-boolean attributes can also have declared ranges of permissible values. Constants (eg dates) are recognised automatically.
Names of entities which occur in facts will be instantiated in dialogues if an entity is declared to be a PERSON, THING or PERSONTHING, and dialogues will then use the correct titles and pronouns for these entities, as well as 'personalising' the dialogues.
4.2.2.	Rule syntax
Rules have a straight-forward syntax which consists largely of declarative propositional statements comprised of facts, constants and a specified set of keywords which provide logical and other operators. Keywords are chosen to have a meaning in English as close as possible to their operative effect, but are represented in full capitals so that both developers and users (who may inspect the rulebase: RULE command) are aware of the logical structure of the rules while nevertheless being able to write and read them as an English paraphrase of the legal sources being represented. Keywords include IF, THEN, ONLY IF, IS, ELSE, EQUALS, IS GREATER THAN, PLUS, MONTHS, and similar words. 
The following simple example shows s135ZK of the Copyright Act 1968 (Cth), and the first three rules representing it.
Multiple copying of works published in anthologies
135ZK. The copyright in a literary or dramatic work, being a work contained in a published anthology of works and comprising not more than 15 pages in that anthology, is not infringed by the making of one or more copies of the whole or a part of that work by, or on behalf of, a body administering an educational institution if:
	(a) a remuneration notice, given by or on behalf of the body to the relevant collecting society, is in force;
	(b) the copy is made solely for the educational purposes of the institution or of another educational institution; and
	(c) the body complies with subsection 135ZX (1) or (3), as the case requires, in relation to the copy

RULE Copyright Act 1968 - Section 135ZK PROVIDES
section 135ZK applies ONLY IF
	the work is a literary work AND/OR the work is a dramatic work AND
	the work is contained in a published anthology of works AND
	the work does not comprise more than 15 pages in the anthology AND
	the work has been copied by the body administering the educational institution AND/OR the work has been copied on behalf of the body administering the educational institution AND
	section 135ZK(a) applies AND
	section 135ZK(b) applies AND
	section 135ZK(c) applies

RULE Copyright Act 1968 - Section 135ZK(a) PROVIDES
section 135ZK(a) applies ONLY IF
	a remuneration notice given by or on behalf of the relevant collecting society is in force

RULE Copyright Act 1968 - Section 135ZK(b) PROVIDES
section 135ZK(b) applies ONLY IF
	the copies are made for the educational purposes of the educational institution OR
	the copies are made for the educational purposes of another educational institution

Isomorphism has been retained to a substantial degree by explicit modelling of the structure of the section (see [Johnson and Mead, 1991], p113, [Routen, 1989], [Bench-Capon and Coenen, 1992]).
The essentially propositional nature of this representation gives it more limited expressive power than a predicate logic representation. However, successful use of an English-like representation of predicate logic has been reported by [SoftLaw Corporation, 1993] and [Dayal, Johnson and Mead, 1994].
5.	Integrating inferencing with hypertext and text retrieval
5.1.	The limits of inferencing
Should we aim to automate legal decision-making, or to provide support for legal decision-makers? The inherently and endemically open textured nature of legal language, whether in legislation, case law or elsewhere is arguably the most difficult problem in the development of legal inferencing systems. It arises in a number of ways ([Susskind, 1987] Ch 5 provides one taxonomy). The lack of fixed meanings of the language of cases and statutes means that it is not possible, even in theory, for an application developer to anticipate all possible factual circumstances which may come within the meaning of predicates used in system dialogues. [Poulin et al 1993] state the problem succinctly: 
In constructing expert systems, one must interpret legal documents ahead of ... concrete applications. This interpretation can only be provisional. The best one can do in constructing the knowledge base is to foreclose as few as possible of the meanings for particular provisions one may ultimately want to consider in concrete situations.
The problem of open texture may be resolved by one or more of three possible reasoning agents: the inferencing engine; the knowledge-base developer; or the end user. Differences over the most appropriate response to this problem leads to different models for the development of legal inferencing systems. 
The most ambitious approach is to develop software and knowledge representations which will suggest solutions (or alternatives) to unanticipated open texture problems. Such research, often associated with the related field of case-based reasoning, presents challenging theoretical problems, and is as yet difficult to implement in systems intended for immediate practical use, despite considerable advances on basic theoretical issues and many different approaches.
Another approach, concentrating on supporting and augmenting the decision-making abilities of the system user to resolve open texture problems, has been advanced by a number of researchers, one of whom sees ‘less need for a cognitive legal machine than for a less sophisticated but more humble product to support intelligent human interaction’ [Stamper, 1988]. Taking this approach, it becomes crucial for legal inferencing systems to allow access to as rich a collection of support materials as possible, so as to support intelligent choices by the user when interpretation of an ‘open textured’ predicate is required (ie when the user has to make a choice which the system is incapable of making). The user needs to be given open-ended access to the relevant supporting materials, rather than for the system to simply direct the user to a few definitions which may assist interpretation (‘closed-ended’ assistance).  
If the user's ability to make interpretive decisions is supported (as distinct from merely recognised as necessary) by inclusion of non-inferencing components, then some of the limitations of rule-based inferencing can be overcome. Susskind reached the rather pessimistic conclusion that research should concentrate ‘on designing systems to solve clear and deductive cases’ ([Susskind, 1987], p192), partly because he did not address how non-inferencing components could be used to deal with open-texture problems. One way to overcome the limitations of such an approach is by making the enhancement of the interpretive resources of users a central aim of legal inferencing systems research. This is one approach to recognising the 'one-to-many representation of knowledge' advocated by [Poulin et al, 1993], though not the approach they advocate.
We take this second approach in the Workstation, by providing various methods by which a user may move from an expert systems consultation to the interpretive materials relevant to that point in the consultation (for example, a statutory definition of a term used in a prompt). Because these relevant materials are presented as hypertext, the interpretive process is open-ended, with the user able to pursue associations, or to conduct free text searches, until the interpretive resources of the system are exhausted. One advantage of this approach is that it may help overcome some aspects of the ‘brittleness’ of expert systems: a user can use the other resources of the system to help ‘work around’ factual variations not adequately dealt with by the expert system itself.
A useful way to view a legal inferencing system, from the perspective of the user,  may be as an interaction between a semi-expert inferencing system and a semi-expert user/interpreter, with control over the course of a problem's solution alternating between the two parties to the interaction. Each does what (s)he or it does best, then hands back control to the other. The program controls those steps in the solution process that are capable of being embodied in a computerised inferencing agent, given existing technology. The  user controls those steps of the solution process which involve abilities which cannot (at least as yet) be so embodied, including the lawyer's various interpretive skills.  
A key practical question  may be to find the boundary between those elements of open texture problems that legal inferencing systems can handle (given existing technology) and those elements that users must provide. Research into non-deductive methods of inferencing is likely, over time, to push back this boundary, but it would be unrealistic to expect it to be removed completely (see [Berman, 1991] for a summary of reasons]. In that case, the approach suggested here may be as valuable for systems that incorporate case-based reasoning as for those that do not.
The rest of this Part of the paper explains how the Workstation addresses these problems of open texture and the relationship between user-controlled and system-controlled aspects of its use.
5.2.	Explanation facilities in the Workstation
The inference engine provides a range of explanations of its inferencing. All of these explanations (except the 'rule' option) are generated by the inference engine reparsing parts of the rule-base on the fly (as discussed in [4.2] above). These explanations do not, therefore, go beyond the content of the rule base. 
A complementary general feature of these explanations is that they are integrated with the hypertext aspects of the system, so that most explanations appear contain hypertext links, allowing the user to go from the terse explanations generated by the inference engine to more expansive source documents and explanatory text.  All of the italicised words in the dialogues below appear as hypertext. [Paquin et al, 1991] comment in relation to their similar use of hypertext that 'The task of designing an algorithm to give a satisfactory explanation to users appears futile because the type of question and the level of answer expected, even from an average user, are far too varied.' Other uses of hypertext to supplement inferencing explanations have been implemented by [Johnson and Mead, 1991], [Konstantinou et al 1993] and [Hafner and Wise, 1993].
The following illustrations are based upon the copyright example in 4.2.2 above. It is assumed that a consultation has been started dealing with section 135ZK and that earlier user responses have indicated that the basic conditions of the head section itself and 135ZK(a) have been met but that the first half of 135ZK(b) has not. 
(a)	Prompts with hypertext 'exits' -  The dialogue continues with the question (prompt) 'Are the copies made for the educational purposes of another educational institution?' in Figure 11 below being generated by the re-parsing of the second condition of the rule concerning 135ZK(b). The hypertext links from prompts (here, to 'educational purpose' or 'educational institution') are the single most important type of hypertext 'exit' from inferencing dialogues because they assist users in resolving open texture problems by assisting them to find appropriate interpretative resources when needed. 

Figure 11: The Consultation dialogue box, with hypertext links from a prompt
(b)‘Facts’ and ‘Conclusions’ displays - Lists of both user-provided ‘Facts’ and system-inferred ‘Conclusions’ can be viewed at any time by selection of the appropriate buttons in the ‘Consult’ window. Selection of a ‘fact’ from the list displayed causes the fact to be forgotten, and inferencing to re-commence after any necessary re-evaluation. If the answer given to the above prompt was ‘no’, the fact would be translated as ‘the copies are not made for the educational purposes of another educational institution’. The conclusions inferred at this point would be translated as ‘section 135ZK(b) does not apply’ and ‘section 135ZK does not apply’. 
(c) ‘How’ explanations of conclusions - Selection of a conclusion from the list displayed causes an explanation of how that conclusion was inferred to be displayed. If the second of the conclusions above was selected, the explanation generated would be ‘Section 135ZK(b) does not apply because the copies are not made for the educational purposes of the educational institution and the copies are not made for the educational purposes of another educational institution’. 
(d) Why’ explanations of questions - Selection of the ‘Why?’ button results in an explanation of why the current question is being asked. If further explanation is available, another ‘why’ button will appear with the explanatory text appropriate to the previous level of backward chaining. If ‘Why?’ is selected in the example above, the following appears:

Figure 12: A 'Why' explanation with hypertext links to the section in question.
(e)‘Rule’ — displays of rules under evaluation - Choice of the ‘Rule’ option (see Figure 11) takes the user to the rule which is currently being evaluated, in a hypertext version of the knowledge-base. Repeated use of the ‘Rule’ option causes previous rules in the backward chaining path to be displayed.
(f)‘What If’ hypotheticals - Checking the ‘What If’ check box (see Figure 11) causes all conclusions which the system is capable of drawing as a result of the next fact provided by the user (and all previous facts known) to be displayed. The box must then be un-checked and a new answer given before the system will proceed.
(g) Reports - On completion of the consultation, the inference engine generates a report based upon the dependencies between premises and conclusions as established by the inferencing process. The report generated in the example discussed above would be: 
Section 135ZK does not apply because section 135ZK(b) does not apply. Section 135ZK(b) does not apply because the copies are not made for the educational purposes of UTS and the copies are not made for the educational purposes of another educational institution.
5.3.	Distributing access to the KNOWLEDGE base
Consultations using the knowledge-base are usually started from within the hypertext part of the system, by three means. If the user is reading a section of an Act or a defined term, a check of the cross-reference index (if one is available) for that section or term will show whether a relevant consultation is available. If it is, inferencing can be started by selecting the 'goal' entry from the cross-reference index (Figure 13). In such situations, the user is able to find the relevant consultation when it is needed, and is alerted to the existence of consultations. A cross-reference index will often contain numerous other related items of information, as well as a 'goal' (as in Figure 8).  Any hypertext links in the text may also be used to trigger evaluation of goals. Third, link text may give the user a choice of inspecting another hypertext node (eg a definition) or evaluating a goal.
Consultations may also be commenced from the list of GOALS (under the 'Consult' menu item), but these are usually limited to the major 'head' consultations within a system, whereas all available consultations can be made available through the hypernet. The difference is between a menu-based and a contextual entry into the knowledge-base.
The hypertext acts therefore as a distributed and contextual interface to the knowledge-base ([Greenleaf, Mowbray and Tyree, 1991]). This approach is also adopted in [Soper and Bench Capon, 1994] (based on 1991 work) who describe their approach as 'animated hypertext'. They have implemented it by the third of the methods described above.

Figure 13: A cross-reference index including a goal
Once the consultation is underway from a cross-reference, the user can go back into the hypertext system via selection of hyperterms embedded in the questions and explanations produced by the inferencing process (Figure 11). Once in the textual materials, the user can again resort to the knowledge-base if necessary, by commencing other consultations from the cross-reference index (or the GOAL list).
Relevant consultations can also be found by searches, because the knowledge-base is searchable. When a rule in the rule base is found as part of a retrieved list, the cross-reference list for that rule will include the 'goal' entry to launch the consultation.
5.4.	User Control over inferencing - a reason for integration
The depth or extent to which a user will want, or need, to use aspects of a legal inferencing system to solve problems will vary. This may depend upon either the extent of the user's domain expertise, the facts of the problem at hand, or both. For example, in an inferencing system on Australian privacy law, consultations concerning compliance with the Information Privacy Principles may depend upon whether the alleged breach is by an ‘agency’. ‘Agency’ has a complex definition which would often result in a lengthy inferencing dialogue. Most likely users could answer without hesitation that the Department of Social Security was an ‘agency’ (ie usage is problem dependent). However, only experienced users would  unhesitatingly answer that the Federal Government Ombudsman was an ‘agency’ -  most would want to use any available inferencing facilities (ie usage is expertise dependent). In other cases, effective resort to textual materials will be far more efficient than running an inferencing session. For example, most users would not immediately know whether the Supreme Court of the Australian Capital Territory is an ‘agency’, but quick reference to the s6 definition of ‘agency’ will make the answer apparent. 
If users are always forced to pursue tedious inferencing dialogues to the bitter end they are unlikely to find inferencing systems efficient, attractive to use, or ‘intelligent’. A system which insists upon dealing with every minor concern at the greatest level of detail is unlikely to be acceptable. On the other hand, users cannot just be left to their own resources to decide how to resolve questions asked of them in inferencing dialogues, but need precise prompts as to the resources they can access (further inferencing, access to commentary, statutes etc).
This problem of user control of depth of inferencing is an instance of a broader principle that, for a knowledge-based system to be of the greatest practical utility, the user needs to be able to exercise various types of control over its operation. The initial invocation of a consultation must be appropriate to a problem at hand. The open-ended nature of interpretative problems means that a user who is attempting to resolve a question asked by an inferencing dialogue may need to run other inferencing sessions unconnected to the primary session as, in effect, ‘sub-consultations’, and then be able to return to the original consultation. To achieve this degree of user control, a wide variety of access mechanisms to inferencing are needed.
5.4.1.	Use of rule types to allow user control
The main use of rule types (see 4.1) is to allow the application developer to limit the depth of backward chaining which occurs automatically. Wherever the inference engine is evaluating a fact, a value for which can be inferred by back-chaining to a  rule which is a declared to be a GOAL, the inference engine will require the user to confirm that further chaining is to occur, and will give the user the option of providing a value for the fact instead. In effect, control of the depth of backward chaining is passed to the user.
This is illustrated by the Consult window below. In contrast with the normal Consult window, the ‘Uncertain’ button has been replaced by an ‘Infer’ button, to indicate that further inferences are available to determine a value for the fact ‘the DSS is an agency’. The hypertext highlighting of the word ‘agency’ also indicates to the user that a definition of the term ‘agency’ can be accessed by selecting that hypertext link. The user is therefore given three choices:  (i) to answer the question without reference to any form of assistance; (ii) to pursue hypertext links to textual materials before answering; or (iii) to use further inferencing to resolve the question.  In this way, the inference engine caters for differences in user expertise, and for differences in the difficulty of the problem at hand.

Figure 14: A consultation window with the 'infer' option shown
It is up to the application developer to decide the extent to which backward chaining should be automatic. However, our general view is that the depth of inferencing should normally be left to the user, at least in relation to those rules which could be considered to commence the evaluation of significant goals in themselves (hence the name ‘GOAL’ for this rule type).
5.5.	Isomorphism and The value of 'English-like' representations
Maximising 'isomorphism', used in the sense of 'creating a well defined correspondence between source documents and the representation of the information they contain [that is] used in the system' [Bench-Capon and Coenen, 1992], has been shown to provide many advantages for the operation of legal inferencing systems: [Bench-Capon, 1989], [Karpf, 1989], [Johnson and Mead, 1989], [Vey Mestdagh, 1990], [Bench-Capon and Forder, 1991], [Bench-Capon and Coenen, 1991], [Johnson and Mead, 1991], [Bench-Capon and Coenen, 1992], [Greenleaf and Mowbray, 1993].
A principal virtue of isomorphic representations is that 'a given source change can be related to a defined fragment of the knowledge base, and that this fragment can be removed from the knowledge base, altered and replaced with confidence that nothing else will be affected by the changes' [Bench-Capon and Coenen, 1992]. This 'atomistic assumption' has been criticised by [Moles, 1991] because 'a change in one rule, which is itself part of the context in which the other rules occur, will give rise to a change in the meaning of those other rules as well' (emphasis added). Moles seems to miss the point: if, for example, a definition is changed in an Act, then of course a change to the rule embodying that definition will change the meaning of the all the other rules in which that defined term occurs. The point about isomorphic representations is that there is no need to make changes to the text of every other rule in which that definition occurs. 
The objection by [Sergot et al, 1991] that some legislation is such a complete mess that it fails to reflect the real structure of the domain is no reason for failing to model it isomorphically, as any attempt to simply model an interpretation of the legislation will make maintenance impossible in the light of legislative changes [Bench-Capon and Coenen, 1992] (see also [Johnson and Mead, 1991]).
We argue in this Part that the benefits which flow from an isomorphic knowledge representation are strengthened and expanded by the use of a (quasi) natural language knowledge representation.  We first argue that an English-like representation can provide a greater degree of isomorphism than a 'formal logic' style of representation. (eg '(under_25) claimant'). We then argue that it also enhances the advantages that [Bench-Capon and Coenen, 1992] see isomorphism as providing throughout the whole life cycle of a system, provided that it is integrated with hypertext and text retrieval as in the Workstation. 
These latter advantages arise principally because of the automatic generation of all aspects of inferencing dialogues by the reparsing of the knowledge-base. As explained in Part 5.2, this includes the generation of prompts, translations of user-supplied and system-inferred fact/values (‘Fact’ and ‘Conclusion’), explanations of questions (‘Why’) and conclusions (‘How’), and construction of a consolidated Report.
5.5.1.	Isomorphism is facilitated by English-like representations
Isomorphism is always a question of degree in legal applications. 'Items in the source', to use the terminology of [Bench-Capon and Coenen, 1992] are not 'givens'. While still laying claim to an 'isomorphic' representation, we could choose to aim for a one-to-one relationship between rules and whole sections of Acts, or sub-sections, or even be more ambitious and try to preserve the clause order within a sub-section. The ultimate isomorphic representation would be for an inferencing engine to be able to interpret raw legal sources (eg an Act) as a knowledge-base. 
Bench-Capon and Coenen, and the definition of isomorphism by [Karpf, 1989] that they approve, only refer to the separate representation and preservation of structure of 'each legal source' [Karpf, 1989] and a 'self-contained unit in the source material' [Bench-Capon and Coenen, 1992]. They very sensibly do not attempt to specify any general rule as to how 'fine grained' these 'self contained units' must be before any claim can be laid to 'isomorphism'. If we take the advantages of isomorphism seriously, then it would seem beneficial to maximise the extent of isomorphism, subject to other considerations such as loss of expressive power.
A rule-based knowledge representation which is in an English-like form, such as the Workstation's inference engine or the representation used by Johnson and Mead in SoftLaw's STATUTE, can have a high degree of isomorphism, both structurally (ie reflecting the logical structure of sections and subsections) and in duplicating much of the actual language used in the legal sources. 
[Bench-Capon and Coenen, 1991], in stressing that their intermediate representation is a formalisation, consider it is 'a necessary improvement on a verbatim representation as recommended by [Johnson and Mead, 1991], as it forces choices of structural interpretation as in [Allen and Saxon, 1991] to be recorded rather than allowing the ambiguities of natural language to pass through the representation'. The English-like representations such as we use or are used by Johnson and Mead are of course formalisations, as described in our case in [4.2] above and in [Dayal,  Johnson, and Mead, 1994] and [SoftLaw, 1994]. 
'Verbatim representation' of predicates ('facts' as we call them) is often possible and we agree with Johnson and Mead that it is desirable as it increases the isomorphism of the representation by facilitating visual comparison. [Poulin et al, 1993] argue that 'because the natural language terms in which the law is expressed are invariably multifaceted, 'translation by a single term is likely to be an impoverishment, even though one uses the same term as the legislator'. This is only so if you consider an inferencing system in isolation, but fails to take into account where a hypertext link from that term is able to give access to a range of interpretations of it.
However, the isomorphism that can be achieved by this means does have limits. It is rare that there can be a 'verbatim representation' of the logical structure of a section or subsection because the natural language of legislation does not restrict itself to the limited range of keywords (operators) and the limited range of logical forms allowed by any representation. Any 'English-like' knowledge representation falls well short of identity with natural language, partly because of the limitations of the logical operators which have yet been developed even in logic programming [Routen, 1989], and ultimately because the choice of operators is still an interpretation [Allen and Saxon, 1991]. The best that can be achieved is usually a logically equivalent paraphrase.
In our representation, the limited range of keywords and their capitalisation in the knowledge representation would make it clear to any reader what 'choices of structural interpretation had been made', while retaining a readable English-like paraphrase. The structure of a SoftLaw rule-base might be less obvious to a casual reader because of  the lack of capitalisation of keywords.
5.5.2.	'Textual baggage' detracts from isomorphism
We argue that isomorphism is also facilitated by the almost complete elimination of prompts, translations, explanations etc being represented separately from rules. To the extent that any of this ‘textual baggage’ ([Johnson and Mead, 1989],  is required by a knowledge representation, there is a loss of isomorphism.
For example, the knowledge representation in the MADE system [Bench-Capon and Coenen, 1992] involves the production of three separate 'strands' of text: the Dialogue Strand (which includes Question Texts), the Object Classes Strand and the Rules Strand. There are links between the Source texts and related items in each Strand, and it is these links which preserve and make transparent the isomorphism of the representation. This approach is consistent with the fourth element of isomorphism defined by [Karpf, 1989], the separation of the representation of legal sources from the representation of queries. It is not clear from [Bench-Capon and Coenen, 1992] how explanations are generated, but it seems likely that there must also be some storage of text strings which are substituted for the attributes used in the rulebase (eg 'compliesWithAct', 'BuildingStructure').
Such an approach is problematic for a number of reasons, also advanced by [Johnson and Mead, 1989b] and [Dayal,  Johnson, and Mead, 1994]). First, the principal virtue of an isomorphic representation is that whenever part of a legal Source changes,  changes need only be made to the corresponding part of the rulebase. Here, corresponding changes will also need to be made to the Question Texts, and also (perhaps) to any text strings stored for explanations. It is true that these additional texts will be linked to the Source in an isomorphic fashion (at least in the careful approach taken by [Bench-Capon and Coenen, 1992]). Nevertheless, the system developer will have to ensure that corresponding changes are made to three different texts which use different words, so the likelihood of error (making changes with slightly different legal effects to two of the texts) or laziness (changing the rulebase but not the questions or explanations) is increased, thus reducing the ease of maintenance. Validation of the knowledge representation is also made more difficult because the expert doing the validation will have to check the Source against two or three texts, not just a rulebase. Development may also be slowed down by the need to develop two or three representations of the same source. Since improved maintenance, validation and efficient development are three of the claims made for isomorphism [Bench-Capon and Coenen, 1992], they may be sub-optimally achieved if this approach is taken .
[Soper and Bench Capon, 1994], in their otherwise useful integration of hypertext and inferencing, don't have hypertext links from their inferencing dialogues, because of the need to 'introduce natural language templates for ... queries'. They say that preliminary studies by [Soper et al, 1993] indicate the value of such links, and that the templates can be 'simply affected'. We question this, for the reasons sketched above.
Our suggestion, therefore, contrary to [Bench-Capon and Coenen, 1992], is that an English-like knowledge representation can increase the extent to which an isomorphic representation is achieved, in comparison with a 'formal logic' representation (assuming that each representation has the same inferencing power, which may not always be so). We will now elaborate on why  this is so.
5.5.3.	Increased transparency  for validation
A (quasi) natural language knowledge-base provides advantages of transparency to the application developer, to domain experts and to the user. Both the application developer and any domain experts can more easily make visual comparisons between the knowledge-base and the legal sources from which it is derived if the knowledge representation (both predicates and logical operators) is close to English and if an approach to modelling legislation such as recommended in [Johnson and Mead, 1991], is followed. This enhances the validation advantages of isomorphism identified in (Bench-Capon and Coenen, 1991] and [Bench-Capon and Coenen, 1992]. Additional validation advantages flow from the elimination of 'textual baggage', as discussed above.
5.5.4.	Increased transparency during use
[Bench-Capon and Coenen, 1992], following [Vey Mestagh, 1990],  correctly argue that 
Many of the problems with expert systems come from a mismatch between the rule based conceptualisation of the expert system and the conceptualisation of the user. Such a mismatch will lead to a user failing to recognise why certain questions are asked, and a dissatisfaction with the rule-oriented explanations that form the typical explanation facilities. Isomorphism should give a correspondence between user and system conceptualisations, and hence make the system more transparent to the user.
If a system's questions and explanations reflect the wording of the legal sources not just their structure, as closely as possible, the user's conceptualisation should be fairly well matched.  This will occur to a high degree where they are generated automatically from an English-like representation which attempts to stay close to the words of the legal sources. However, the extent to which  users will recognise the source of questions generated from the words of obscure subsections will vary with their expertise. In this situation the Workstation's 'Why' facility (Figure 12) provides what the user needs: a terse explanation of why the question is asked, citing the subsection, with a hypertext link to the text of the sub-section and its context. This is a superior solution to any attempt to incorporate a 'canned' explanation in the knowledge representation, for reasons previously explained (see [5.1]). The same reasoning applies to the WorkStation's  'How' explanations and its reports.
Providing the user of the application with open-ended access to the knowledge-base during the operation of the system (as is done through the ‘Rule’ command and hypertext browsing) is desirable for several reasons. Most obviously, it allows the user to check for completeness and accuracy. A transparent knowledge-base helps to demystify and explain exactly what the system can and cannot do. It assists with user confidence in the results generated by problem sessions and discourages blind reliance. This depends upon the knowledge-base being intelligible to likely users, which requires some English-like representation.
5.5.5.	Increased explanatory power  
The integration of the inferencing component with hypertext and text retrieval increases the explanatory power of what are otherwise necessarily terse explanations (and prompts) because they are automatically generated from the rule-base.  Illustrations using the various explanation facilities in the WorkStation software are in 5.2 above. This increased explanatory power can be obtained by largely automated means by reliance on the natural language connections between the knowledge-base and the explanatory texts.
5.5.6.	Benefits for application development
An application developer does not have to give any significant amount of attention to the development of the ‘textual baggage’ involved in prompts, translations, explanations etc. If the application developer can concentrate solely on writing a rule base, this should lead to more efficient application development. If the rule base can be made as close as possible to a paraphrase of the legislation (or other source), this should also cause more efficient development. [Bench-Capon and Coenen, 1991] found that ‘adopting the principles of isomorphism results in a very disciplined and teachable methodology’, but this may apply even more strongly to knowledge representations which are also English-like rather than symbolic according to [Johnson and Mead, 1991] and [Dayal et al, 1994]. 
A related question is that of the role that lawyers should play in the development of legal knowledge-bases. If it is the case that the process of developing a legal knowledge-base involves such an irremovable and constant process of both interpreting legal sources [Allen and Saxon, 1991] and avoiding imposing interpretations not required by the source, then it is questionable whether lawyers and/or domain administrators should only be involved in knowledge-base validation (which is all that it suggested in  [Dayal et al, 1994] or implied in [Bench-Capon and Coenen, 1991]). It may be that it is more efficient if it is made possible for lawyers to be involved directly (though not necessarily alone) in the writing of  legal knowledge bases [Greenleaf et al, 1987]. If so, it is more likely that appropriate legal domain experts will be willing and able to master an English-like knowledge representation.
5.6.	Isomorphism and Developer control of inferencing
By default, all  rules in a Workstation rule-base are both backward chaining and forward chaining daemons, usually written in a purely declarative fashion. However, an application developer may decide to place restrictions on how rules will behave by declaring rules to be of  various types (PROCEDURE, DAEMON, or FORWARD), or as having qualifiers  (GOAL), thereby modifying the normal course of inferencing. This may be done in order to create more efficient inferencing in the particular application, in which case it represents the embodiment of application-specific heuristics. It may also be done in order to allow the user (in a specific application) to assume greater control over the course of inferencing than the default behaviour would allow. 
In either case, the application developer is, in effect, imposing a level of procedural control over an otherwise declarative rule-base and an unconstrained inferencing system. This could be considered to detract from the declarative nature of the rule-base, and also from its isomorphism (see [Bench-Capon and Coenen, 1992] for similar warnings). 
However, it is possible to address these problems by using appropriate care in the utilisation of these 'control features'. We have two general guidelines for the use of rule types. First, it is best to initially write the rule base only with non-typed rules (ie, in default, both backward and forward chaining), then observe its behaviour and only modify it to the minimum extent necessary. Second, the use of heuristic rules and rules which include any procedural elements should be minimised, and, where possible, they should be kept separate from the declarative rules which express legal sources (see [Johnson and Mead, 1991] and [Dayal, Harmer, Johnson and Mead, 1993]).
Some examples of use of various rule types follow. DAEMON is the only rule type which has any frequent use, particularly in relation to heuristic rules. For example, in a propositional knowledge representation, it may be useful to have a heuristic rule that provides that if a work is a literary work it is not an artistic work (if this is legally appropriate). However, if the system needs to determine whether a work is an artistic work, then it would be pointless for it to back-chain to this rule and ask whether the work is a literary work. The use of DAEMON turns the back chaining off. Such heuristics are often interpretations of copyright law as a whole by the rule-base author, and should be kept in a separate part of the rule base from the declarative representation of the legal sources [Dayal,  Harmer,  Johnson, and Mead 1993].
FORWARD rules are rarely used but can be valuable. Such rules are, in effect, heuristics to increase the efficiency of inferencing by embodying known ‘short cuts’ to answers. For example, there are many laws concerning government agencies that only apply to the Australian Security Intelligence Organisation (ASIO) in very restricted circumstances. If, in an application concerning these laws, the system determines at any stage that it is dealing with ASIO, it may be valuable to have a FORWARD rule which is triggered and interrupts what would otherwise be the normal chain of inferencing in order to determine the values of its other conditions, even if this involves asking the user to provide fact values. 
A number of other minor features allow elements of control. In backward chaining, where two or more rules have as conclusions the fact that is being evaluated, the inference engine evaluates these rules in the order in which they appear in the knowledge-base. If this order needs to be changed, an ORDER statement can be used.
In some cases, control elements only alter the extent of inferencing or reporting of conclusions. The developer's use of GOAL qualifiers to rules to enable the user to control the depth of desired inferencing has already been discussed in [5.4]. We do not consider that this detracts from isomorphism because its effect is transparent to the user, and to any expert validating the knowledge-base.
Another feature of this nature is the use of the boolean non-conditional operators OR/WITH and AND/WITH, which cause the inference engine to determine all grounds on which a conclusion is supported, not just sufficient grounds to justify it. Their use changes the inferencing behaviour, but only in the sense of prolonging the consultation and the comprehensiveness of the report presented to the user. It would probably be desirable if the developer's use of such operators also resulted in the user having the option of whether to evaluate additional grounds on which a decision can be supported. Otherwise, the developer's use or omission of these operators is not transparent to the user.
6.	Multi-modal inferencing and integration
The Workstation's inference engine supports 3 types of inferencing: backward and forward chaining rule-based inferencing, event-driven automated document generation, and a form of case-based inferencing. As such, they are instances of the three main uses of inferencing which have been common in the legal domain, though they are not the only possible types (eg use of inferencing to generate text searches).
Our main interests in document generation and case-based inferencing have been to explore (i) how all three types of inferencing may interact; and (ii) whether integration with hypertext and text retrieval gives similar advantages for document generation and case-based inferencing as it does with rule-based inferencing.
6.1.	Document Generation and the inference engine
In a survey of commercial document assembly tools, Lauritsen notes that document drafting is 'regrettably unexplored in cognitive science and AI circles' [Lauritsen, 1992]. He notes that [Gordon, 1989] criticises conventional document assembly programs for, among other reasons, 'that they lack the advantages of declarative knowledge representation'. Lauritsen comments that 'they are still following the procedural markup paradigm associated with Sprowl [Sprowl, 1979], wherein master documents are expressed in terms of if-then structures, repeat loops and variables' [Lauritsen, 1993].
CAPS (from Capsoft Development Corporation), one of the two programs Lauritsen reviews, relies on a procedural command language [Lauritsen, 1992]. This is typical of most legal document generators, and is the case with WorkForm (from Analytic Legal Programs Inc) and was the case with Document Modeler. Hypertext-based document assembly programs [see Wilson, 1991] also assemble documents by what is, in effect, a decision network.
In a survey of the features of many of the earlier document generation systems, Brooks suggests '20 key points' for comparison [Brooks, 1989, p116], but the incorporation of any element of declarative representation, or integration with rule-based inferencing, is not among them. It seems to be assumed that document generators should operate in a procedural fashion, using IF-THEN-ELSE structures to determine a branching, and in effect constructing a decision network. 
The other program Lauritsen reviews, Scrivener (from Dianoetic Development Company) [see also Evans, 1990], is unusual in that 'rules link one or more conditions (using the logical or as well as and)with one or more results in a rule-base of IF-THEN-ELSE structures. These rules are invoked when the system is building a document and encounters a variable for which it does not yet have a value. Scrivener uses a traditional backward chaining method which may result in other variables becoming sub-goals. Only after searching the entire rule-base is the user queried. Automatic 'why' explanations can be generated. ' [Lauritsen, 1992, p91]. The Workstation software's implementation of document assembly is similar in this respect to Scrivener.
Extensive use of backward chaining is also made by [Branting, 1993], in a system designed to generate documents providing judicial justifications for decisions. Each user-selected backward chaining goal has text templates associated with it, which provide a partially procedural control over document construction.  
6.1.1.	Workstation implementation - inferencing
Documents are generated by declaring rules of type DOCUMENT. They differ from other  rules only in that the statements PARAGRAPH, LINE and TEXT are available to write paragraphs to documents. DOCUMENT rules must be invoked as a GOAL or explicitly called (via the CALL statement) from other rules, as they are not invoked by chaining. 
An example of a DOCUMENT rule and a related rule in the rule base follows:
DOCUMENT Contemplation of Marriage PROVIDES
IF this Will is to be made in contemplation of marriage AND
the Will is to be conditional on the marriage actually taking place THEN
	IF section 14(2) of the Wills Act 1970-1971 (WA) applies THEN
		NUMBERED PARAGRAPH This will is made in contemplation of my marriage with <the name of the testator/testatrix's fiancee>.
	ELSE
		NUMBERED PARAGRAPH This will is made in contemplation of my marriage with <the name of the testator/testatrix's fiancee> and is conditional on the marriage taking place within <the maximum number of months before the wedding> months.
...

RULE Wills Act 1970-1971 (WA) Section 14(2) PROVIDES
section 14(2) of the Wills Act 1970-1971 (WA) applies ONLY IF
	the testator/testatrix is domiciled in Western Australia AND
	the testator/testatrix does not own immovables in another State

Document generation is integrated with the other inferencing components through the use of common fact names in the conditions of document rules. If a DOCUMENT rule needs to evaluate a fact in order to determine which text to generate for a document (for example, whether ‘section 14(2) of the Wills Act 1970-1971 (WA) applies’), it will attempt to evaluate that fact by backward chaining to the rule base, just as any other rule would do. Document generation can therefore utilise fully any knowledge base which has been developed in the same area of law, with all the benefits of declarative programming.  In particular, the legal preconditions for the generation of any particular part of a document can be included as part of a rule-base without significant consideration of their procedural role in document generation. 
Any text which is embedded in the document text in angle brackets (eg '<the maximum number of months before the wedding>) is also treated as a fact for which a value must be obtained, either by inferencing or by asking the user.
The inferencing engine's 'Forget' command can be used to forget specified facts and then generate alternative versions of documents.
6.1.2.	Integration with hypertext and text retrieval
The document generation component also benefits from integration with the other Workstation components, and particularly with hypertext links from terms in queries to the user, the answers to which determine the structure and content of the generated document.  The main reason for this is that document generation poses the same demands on the user as any other form of legal inferencing for the user to make interpretative decisions in order to determine whether a particular transaction satisfies the conditions for particular document clauses to be generated. No matter how comprehensive a document template purports to be, if the document derives from legal issues of any significant difficulty, then the open texture of legal language is likely to require the user to make significant interpretative decisions. If so, then any pre-determined amount of text which is provided to the user to assist with these interpretative decisions is likely to be inadequate (for the same reasons we advance in relation to expert systems). As Lauritsen argues in a somewhat different context, 'the realm of document knowledge is vast' [Lauritsen, 1993, 189].
Such open-ended access to reference materials is not yet common in document generation systems. For example, 'Reference information in Scrivener is static, bound to specific variables, only one level deep, and only available in the context of questions... In other words, Scrivener systems presently offer explanatory or other auxiliary material only in the form of one module of fixed text for any given question...' [Lauritsen, 1992]. In contrast, 'CAPS hypertext-like resources can be referenced anywhere (including within other resources), and be as dynamic and interactive as any other part of a CAPS system' [Lauritsen, 1992].
Other advantages for document generation also follow from integration with hypertext and text retrieval, including the following:	
	•		a hypertext network can be used as a contextual and distributed front-end to assist users to find and select available and appropriate document templates (eg a Form in an Act can have a template for generation of the form as a cross-reference);	

•	free text searches for relevant documents or clauses is likely to be valuable because system developers may not anticipated all contexts in which particular clauses may be useful.
[Lauritsen, 1993, 189] advocates a different approach to achieve many of the same benefits (and others): the representation of 'document models' in SGML (Standard Generalised Markup Language - ISO 8879) so that such models would be 'knowledge enriched' and support 'multiple modes of use' including hypertext browsing and searching.
6.2.	'Examples' and the inference engine
6.2.1.	The WorkStation's PANNDA implementation
The Workstation's inference engine also supports a limited form of analogous or case-based reasoning based on Tyree's PANNDA algorithm, with some modifications.   When the inference engine can find no further rules to assist in obtaining a value for a fact, it will look for an ‘example set’,  a set of cases (or other examples) which each give a value for that fact as their conclusion (the common outcome). The Workstation's PANNDA then infers (or asks the user) for a value for all facts used in the examples for the user's problem. Finally, it compares each example with the situation described by these fact values and finds the nearest and furthest example. It reports the nearest example as the most likely precedent to follow, listing those factors that it has in common with the user's problem and those it does not. The furthest example is the one with the closest facts but giving a different result to the nearest one (ie the example which most requires to be distinguished).
The PANNDA approach does not  represent a new approach to case-based reasoning (see [Branting, 1994]), and we have not used it in commercial applications. More recent research such as [Ashley, 1990] has utilised multi-level representations of cases, in contrast to the flat representation used by PANNDA. The principal use in the Workstation of the PANNDA component is for exploration of the benefits of integration of case-based inferencing with other Workstation components, and of common means of representing examples,  rules and document templates.
Examples have a similar syntax to rules:
EXAMPLE Armory PROVIDES
the finder wins BECAUSE
	the finder was not the occupier of the premises AND
	the chattel was not attached AND
	the non-finder was not the owner of the real estate AND
	the non-finder was not the owner of the chattel AND
	there was a bailment of the chattel AND
	there was not a term in a lease which mentioned  found items AND ....
[and so on for other facts of the case]

6.2.2.	Integration with other inferencing forms
Integration of PANNDA with rule-based inferencing and document generation occurs in a number of ways. To find a value for an unsatisfied fact in a rule-base, the inference engine looks in default for other rules it can back-chain to, and then for any EXAMPLE set that has that fact as a common conclusion. Similarly, in order to determine a value for a fact used in the conditions of an EXAMPLE set, the inference engine will first resort to backward chaining to the rule-base (and, for that matter, to any other EXAMPLE set having that fact as a conclusion), before it asks the user to provide a value.  EXAMPLE sets may also be invoked when a fact with an unknown value occurs in a DOCUMENT template. More complex strategies to combine rule-based and case-based inferencing have not been implemented in the WorkStation software.
The use of the inference engine's (quasi) natural language knowledge representation for PANNDA also has some other potential benefits. 'Isomorphism' is not commonly used in the context of case-based representations, but the arguments in favour of isomorphism (see [5.5] above) seem of equal application. A single case may be relevant to multiple issues in a single legal domain, some of which may be procedural rather than substantive [Berman and Hafner, 1991], some of which may be appropriately represented as rules, and others which may only be appropriately represented in a case-based representation. It is desirable if (as in PANNDA), all of these aspects of the representation of a case can be represented together in a knowledge-base, so that the full interpretation of a case can be seen. Together with the automatic generation of inferencing dialogues so as to eliminate 'textual baggage', these elements give the development of PANNDA applications the same benefits of increased development efficiency, transparency and explanatory power, and reduced maintenance, as rule-based representations. However, whether a more powerful method of case-based inferencing could be developed which shares these representational advantages remains to be seen.
6.2.3.	Integration with hypertext and text retrieval
Since the Workstation's EXAMPLE-based inferencing shares both the same user interface as the  rule-based inferencing, and a (quasi) natural language knowledge representation, it has the same degree of integration with the Workstation's hypertext and text retrieval components. For example, any terms which appear in prompts may provide open-ended hypertext access to text. Similarly, where cases are referred to in any system-generated explanations, hypertext links to case summaries or text can augment these explanations. 
For those concerned with how case-based inferencing can be incorporated into systems in practical use in the near future, the benefits of such augmentation may be as significant as we argue they are with rule-based reasoning. Case-based inferencing systems usually attempt to deal with 'open texture' problems of legal reasoning, so it may seem paradoxical or otiose to suggest that they could benefit from an approach which is designed to place the interpretative burden in open texture reasoning on the system user. However,  case-based inferencing systems are likely to be difficult and expensive to build, even in comparison with rule-based systems, and so are likely to provide inferencing to deal with a relatively narrow range of open texture issues. If we assume that a user of a case-based inferencing system provides the facts of a test case in an interactive dialogue with the system, then that dialogue is likely to pose questions to a user which are (in some factual circumstances) 'hard questions' requiring interpretation of open-textured predicates. 
6.2.4.	Suggestions for the practical use of case-based reasoning
We suggest that there are a number of aspects of the WorkStation's approach to case-based inferencing which could usefully be incorporated into systems in practical use in the near future, including some form of (quasi) natural language representation, and hypertext links from inferencing dialogues.
7.	Future work on integration
7.1.	Exploiting common elements in different representations
Different ways of representing and using law for the purposes of different engines nevertheless pose many common problems. If these problems can be solved in a way which is useable by more than one representation, this gives a significant gain in the power of a knowledge representation. Furthermore, if similar choices are made in relation to similar elements in different representations, use of the system will become more intuitive to the user. 
The following table and discussion sets out some examples of common representational requirements,  how they are dealt with in the different representations, and some suggestions for integration. It is only an illustration of how it can be valuable to look at the same issue, and possible solutions, across a number of representations, and is not comprehensive.
Table 2: Some common representational issues
Some common representation  issues	Hypernet (hypertext )	Concordance (retrieval )	Knowledge-base (inferencing)	Possible integrated implementation	
Granularity	node definition	document definition	defining isomorphic units of source text	identical nodes and documents in default	
Context  as limitation	browsing limits

scope of implied links	user-selectable search areas	

scope of attribute usage	no obvious integration
implementation uncertain	
User orientation (overviews)	landmarks	database menus	listed goals	landmarks include start of search areas  and  listed goals 	
User orientation  (history)	browsing history lists	prior search lists	active inference list	common history list	
Intellectual indexes	access structures	indexed fields	attribute selection	browsable lists of index terms or attributes which launch searches	
Thesauri	aliases	synonyms	identity heuristics	common alias and synonym lists	
Term occurrences	relevance ranking	links created from inference networks [Savoy, 1994]			

Some of these forms of integration of common elements are easier to achieve than others. 'Granularity' refers to what is chosen to be the basic unit of text retrieval for particular types of material, and the same problem arises in defining the best node size in a hypertext system, and in defining what unit of legal source material will be represented in an isomorphic fashion in a knowledge-base. In dealing with legislation the choice is normally between sections and subsection, but the important thing is that the same choice should be made for all three if possible, so as to reduce user disorientation. 
Similar considerations will usually dictate that devices to give users an overview of the whole system will have a similar scope. In our experience it is usually valuable to make the start of any searchable chapter of a database also a hypertext landmark. The major goals of the inferencing system could also be usefully included in the landmark list. In similar fashion, it is valuable to place prior nodes browsed,  prior searches and prior inferences on the hypertext history list, so users only have to look in one place in order to go back to anything they have done in that session (see Figure 5).
Intellectual indexes of the contents of a legal information system, if available, provide one of the most widely used and valuable forms of embodying knowledge about those contents. The value of separate fields for catchwords, headnotes, abstracts  and other forms of indexing is well recognised in text retrieval. These same indexes can be re-presented in hypertext systems to provide access structures in the form of  indexes of hypertext links to indexed material, or to create a self-updating index (see Part 3.2), or to create a cross-reference structure between similarly indexed nodes. The creation of a knowledge-base, whether rule-based or case-based, also almost always involves 'indexing' every section or case against a controlled set of attribute descriptors (to ensure consistent attribute representation). If an English-like knowledge representation is used, these attribute descriptors may overlap significantly with catchwords or other index terms. The process of knowledge-base creation may therefore create a valuable set of search terms or hypertext links (see [Soper and Bench-Capon, 1994] for the latter). Conversely, the University o f British Columbia FLAIR team has attempted to utilise concept indexes of legal cases (extracted automatically in this case) to provide the attributes on which a case-based reasoning system is based: [Gelbart and Smith, 1992], [Deedman, Gelbart and Coleman, 1992].
An enormous amount of research has gone into the means by which document dependencies and term dependencies may be most effectively utilised in text retrieval, whether these dependencies are identified through intellectually created thesauri, or through the automated analysis of texts for factors such as word occurrences, document clustering, citation relationships, or more complex probabilistic relationships based on inference networks [Turtle and Croft, 1991]. Considerable recent attention has also been directed to the utilisation of such dependencies in the construction of hypertext links [Croft and Turtle, 1989], and in systems where hypertext links enhance the effectiveness of text retrieval [Savoy, 1994].
7.2.	Terminology and theory for integration
There is still a need for a consistent and comprehensive conceptual framework and terminology for the computerisation of legal information, identifying where law's needs are distinct from those of other domains. An analogy to the development of the cinema has been used to illustrate the idea of a hypertext as a new  grammar for text ([Stephen and Schreiber, 1989] quoting  [Anderson, 1989]). The potential of the new technology of film was at first little understood, to the extent that cameras were bolted down and all shots taken from a central, fixed, perspective. It took many years for film-makers to develop the ‘grammar of motion pictures’ [Anderson, 1989] with which both viewers and film-makers are familiar today, through such contributing techniques as moving cameras, close-ups, flashbacks (a non-linear technique), aerial perspectives and slow motion.
A similar 'grammar'  for legal computerisation needs to be understood both by application developers, so that they can communicate better with each other, and by the ‘audience', the users of computerised legal products. Film audiences may have been confused by film sequences out of their correct temporal order until the concept of the ‘flashback’ became one that they understood and were comfortable with. A founder of hypertext states ‘we have to invent other document forms that somehow become standard so that people have pattern recognition and say “Ah, yes, I know how that one works” [van Dam, 1988]. The Macintosh operating system and Microsoft Windows have gone a long way toward establishing a standard for graphical user interfaces that users now recognise and require. As mentioned earlier, a number of www browsers have established a general set of hypertext features which are widely accepted and expected. Styles of application development appropriate for law will give us that recognition in the legal domain.
Each ‘mode’ or paradigm approach to computerisation, such as expert systems, hypertext, etc has computing ‘techniques’ characteristically associated with it, such as inferencing by backward chaining rules (in expert systems), backtracking (in hypertext), or word occurrence proximity searching (in text retrieval). Most computing modes and techniques are not peculiar to law, but there may be characteristic styles in which they are used to produce effective legal applications. For example, the ideal of  isomorphism in legal knowledge representation is not dictated by any inferencing engine or its knowledge representation, but derives from arguments about what makes a legal expert system legally sound. Both developers and users need to have common expectations concerning isomorphic representations before users will make the best use of them. Similarly, hypertext facilitates but does not dictate cross-referencing structures such as we and others have used, but some form of such structures may be needed for effective hypertext applications based on legislation. 
The ways in which available techniques are combined by application developers into applications that are powerful but intuitive is a matter of style, distinguishing good applications from bad ones. The literature on legal computerisation has concentrated on new techniques, but we also need a literature on good style in legal applications development.
Acknowledgments
This research has been assisted by grants from the Australian Research Council to Graham Greenleaf, and from the Committee for the Advancement of University Teaching (CAUT) to Graham Greenleaf and to Andrew Mowbray. Alan Tyree  collaborated on earlier stages of the DataLex work, and has continued to provide valuable comments and advice. Patrick Gunning was involved in development of the early WorkStation applications. Marc Lauritsen, Don Berman, and our anonymous reviewers, each made constructive suggestions. Special thanks to Carole Hafner for invaluable comments on the whole paper, though the responsibility for errors remains ours. Don Berman and Carole Hafner provided one of the authors with a congenial and stimulating environment at Northeastern University  during the revision of the paper. 
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