=Paper=
{{Paper
|id=Vol-465/paper-10
|storemode=property
|title=Learning and Verification of Legal Ontologies by Means of Conceptual Analysis
|pdfUrl=https://ceur-ws.org/Vol-465/paper10.pdf
|volume=Vol-465
}}
==Learning and Verification of Legal Ontologies by Means of Conceptual Analysis==
https://ceur-ws.org/Vol-465/paper10.pdf
Learning and Verification of Legal Ontologies
by Means of Conceptual Analysis
Erich Schweighofer
Centre for Computers and Law
DEICL/AVR, Faculty of Law, University of Vienna
Schottenbastei 10-16/2/5, 1010 Wien, Austria
Erich.Schweighofer@univie.ac.at
rechtsinformatik.univie.ac.at
Abstract. A combination of intellectual input, NLP tools and appropriate
ontological representation may overcome the existing bottleneck of legal
knowledge acquisition of legal ontologies. Such semi-automatic tools rely on
easily available input, extensive iterative semiautomatic checking and refining of
this knowledge. Preliminary results using the tools of SOM/GHSOM,
KONTERM and GATE show the feasibility of this method. However, it remains
to be seen if a sufficient number of legal writers will adapt to this new
workbench.
Keywords: Legal ontologies, text analysis, learning, conceptual indexing
1 Introduction
In law, indexing was and is still a very important tool for coping with the vast body of
legal materials. Since the advent of information retrieval, legal full text search has been
added to the methods of legal research. However, an index of concepts or legal sources
is still considered as the best access to the sequential structure of handbooks, textbooks
or collections of materials. Such indices may be also used for the production of
summaries of cases (head notes) identifying the important parts of court decisions.
Huge reference systems on legal materials also exist either based on citations (e.g. the
Austrian index [1] or thesauri (e.g. the Swiss thesaurus [2]).
In previous papers, we have argued for the creation of a dynamic electronic legal
commentary [3]. Differently to a handbook (or commentary) as the most advanced
traditional form of explicit knowledge representation, the dynamic electronic legal
commentary is based on legal ontologies as major knowledge base and integrates
semiautomatic means of semantic indexing. A complete knowledge representation of a
legal domain requires many resources, either that of legal experts or those of ICT.
Given the dynamic change in law, means of semiautomatic creation and verification of
ontologies are thus highly needed for cheaper and faster efforts of compilation and
analysis.
Such an approach consist in “working together” between legal experts and
ontological tools. Only experts can easily produce the extensive input and check the
vast semiautomatic output. However, up to now, legal writers still prefer intellectual
analysis without semiautomatic means.
Legal ontologies should be the core of such a knowledge base. However, these
ontologies are either too broad and shallow (e.g. LOIS and DALOS) or too small and
deep (e.g. LRI Core) in order to meet the standards of semantic indexing. Thus, we
propose the development and refinement of such an ontology by means of conceptual
analysis.
The remainder of this paper is organized as follows: section 2 describes related
work, section 3 gives an overview on the method. In section 4, the status of
implementation and problems are discussed. Section 5 contains conclusions and future
work.
2 Related work
The main components of legal knowledge are the legal retrieval system (or legal
information system) as a huge text corpus with a (mostly) textual representation of the
legal order and meta knowledge about the text corpus. Computationally speaking,
meaningful semantic indexing is linked to a legal text corpus. Such indexing exists in
legal brains, legal books but also legal knowledge bases. Legal structuring as such is
done by lawyers, in their minds, and is presented and made explicit in their
argumentations and writings. As a product of this process, a legal commentary is
considered as the highest level of this endeavour.
The semantic web can be considered as an extension to the current web in providing
a common framework that allows data to be shared and reused [4]. Semantic search
may also improve disappointing results of present legal information retrieval [5].
Thesauri (or legal dictionaries) are getting more importance now as a traditional tool
for representation of knowledge about legal language use. A thesaurus for indexing
contains a list of every important term in a given domain of knowledge and a set of
related terms for each of these terms [6]. A lexical ontology builds up from this basis
with works on glossaries and dictionaries, extends the relations and makes this
knowledge computer-usable in order to allow intelligent applications. More advanced
representations may formalize complex legal rules and conceptual structures.
Ontologies [7] constitute an explicit formal specification of a common
conceptualization with term hierarchies, relations and attributes that makes it possible
to reuse this knowledge for automated applications.
Legal knowledge representation remains the most important and challenging task of
legal ontologies [8]. The frame-based ontology FBO of [9] and [10] as well as the
functional ontology FOLaw [11] can still be considered as important work on
formalisation. More advanced work exists in the development of a core legal ontology
called LRI-Core [12] or the impressive standard for the development of a legal
ontology called LKIF Core Ontology (Legal Knowledge Interchange Format) [13].
Quite many projects were focused on conceptual information retrieval (see e.g.
Iuriservice [14], LOIS (Lexical Ontologies for legal Information Serving) project [15].
The Legal Taxonomy Syllabus [16], DALOS [17] or the Comprehensive Legal
Ontology (CLO) [3].
Such powerful ontologies can only be built if resources of robust NLP and machine–
learning are exploited. We share the view of [18] that such technologies are “the key to
any attempt to successfully face what we termed the acquisition paradox”. However,
we argue that the quite huge experience of semi-automatic text analysis and conceptual
indexing in law (see e.g. the projects KONTERM/LabelSOM/GHSOM [19, 20],
SALOMON [21], FLEXICON [22], SMILE [23], or Support Vector Machines [24])
should be taken into account and reused.
The automated linking of documents constitutes the most advanced work in
semantic indexing (e.g. AustLII [25], CiteSeer [26]). It has to be noted that the task is
easier due to more formalized language and a controlled vocabulary.
3 Idea and Method
This method adds the idea of an ontological workbench for the lawyer to the already
existing tools. A combination of expert knowledge, easy access to intellectual input and
the use of semiautomatic refinement empowers this method for contributing to solve
the “scaling up”-problem. It should be noted that legal writing consists to a large
degree in structuring, refining and representing the content of legal text corpora in an
abridged and more abstract way. So far, legal writers are still not much in favor of
semi-automatic analysis as a tool for improving efficiency to this very time-consuming
process.
Our method combines intellectual input, corpus-based methods of verification and
refinement as well as text categorization, conceptual analysis and text extraction. Using
our expertise of as a lawyer with extensive practice and an academic in legal
informatics, we are developing a workbench for other lawyers for NLP techniques and
text analysis.
Due to this corpus-based approach on legal analysis, all (tentative) results have to be
checked against a legal text corpus. In our case, the millions of documents of the
Austrian legal retrieval system RIS (Rechtsinformationssystem des Bundes) [27] and
related private databases RDB and LexisNexis are used for improvement, refinement
and verification of the ontological representation.
As a start, we give a sketchy picture for a sufficient granularity of an ontological
representation of a jurisdiction: about 10 000 thesaurus entries, 5 000 citations, up to
200 document types, a classification structure (e.g. RIS classification or EUR-Lex
classification codes), 100 text extraction and summarization rules, and, as
representation of the dynamic legal electronic commentary, an indefinite number of
concepts, rules and procedures. It is an enormous body of knowledge and it should be
clear that a stepwise approach has to be taken, e.g. a start with descriptor or citation
lists that will later be transformed into ontological representations. The final version,
the dynamic legal electronic commentary, will take some time to finish.
The target – the ontological representation – should consist of the following meta
data that has to be maintained in a database with different types of knowledge units (or
tables):
Thesaurus entries: header, definition (with sources), examples (with sources),
relations (synonym, homonym, polysem, hyponym, hyperonym, antonym etc.),
classification, other information.
Citations: header, identification (abbreviation or number), synonyms, classification,
author, other information.
Document types: header, identification (abbreviation), use, format, other
information.
Classification: header, code, definition, relations, other information.
Extraction and summarization rules: header, rule, definition, relations, other
information.
Concepts: header, definition (with sources), related thesaurus entries and citations,
relations (synonym, homonym, polysem, hyponym, hyperonym, antonym etc.),
classification, legal conceptual structure (ontological model), other information.
Rules: header, quasi-logical expression, source, type, classification, legal conceptual
structure (ontological model), other information.
Procedures: header, decision tree, source, type, classification, legal conceptual
structure (ontological model), other information.
For the start, we have collected available information on legal meta knowledge from
traditional sources. Concept lists were taken from the table of contents and indices of
text books and commentaries. A quite complete citation list was provided by the
Federal High Court of Administration and improved using the reference book. The list
of document types reflects the present status of documents in the legal information
system RIS. Text extraction rules were intellectually created by studying the linguistic
styles and patterns of Austrian laws, judgments and literature. We took also advantage
of the experience with the LOIS project. With this method, it was quite easy to achieve
a sufficient but still rough representation of conceptual structure of the Austrian legal
order. For easier re-use, this information was incorporated in a relational database.
Data may still quite incomplete at the beginning but must be sufficient for
semiautomatic analysis. An XML representation is also available for later
incorporation in higher representations, e.g. the knowledge base of the dynamic
electronic legal commentary.
As tools of semi-automatic analysis, we have implemented the modified GHSOM
method of classification, the KONTERM method of conceptual analysis, and the
GATE methods of ANNIE and JAPE [28].
The modified GHSOM method is based on the self-organising map, a general
unsupervised tool for ordering high-dimensional data in such a way that alike input
items are mapped close to each other. In order to use the self-organising map to explore
text documents, we represent the various texts as the histogram of its words with a
TFxIDF vector representation. The methods LabelSOM can properly describe the
common similarities of the cluster. An extension to the SOM architecture, the GHSOM
[20] can automatically represent the inherent hierarchical structure of the documents.
An extension for legal purposes allows the manual refinement of vector weights of the
documents with data enrichment tools. The produced output consists in structured maps
of clusters with cluster descriptions. These descriptions were used for refinement of the
thesaurus, in particular for completeness and for synonyms.
The KONTERM method [3] produces structured lists of term occurrences with a
description of the various meanings. These representations were incorporated in the
description of homonyms and polysems of thesaurus entries.
The GATE JAPE tool (Regular Expressions Over Annotations) is implemented for a
similar purpose. It is much more powerful in bigger text environments but does not
allow so sophisticated representations of meanings as the KONTERM method.
The GATE ANNIE (A Nearly New Information Extraction System) tool supports a
more detailed analysis: segmentation of documents (tokenizer), words, gazetteer,
sentence splitter and semantic tagger.
These methods have one big advantage and two important disadvantages. The huge
text corpus of materials can be explored and analyzed with much higher accuracy,
speed and efficiency. All ontological concepts can be checked for meanings,
definitions and relations in the legal information system. However, the semiautomatic
output is quite voluminous and analysis takes some time. Further, it represents only an
intermediate step in the process of analysis. It must be mentioned that these tools are by
far not sufficiently adapted for a legal environment. Legal experts may refuse the use
for the simple reason of an inconvenient interface. However, in the hands of a
supportive and experienced expert, such tools prove to be very helpful and may
substitute other research.
4 Implementation Details and Problems
The test environment consists of the Austrian legal order, its textual representation
in the retrieval system RIS (Rechtsinformationssystem des Bundes, Austrian legal
information system) and the “rough ontology” of thesaurus entries, citations and
extraction rules.
This very “rough ontology” was checked and refined by selective document corpora
analyzed with GHSOM, KONTERM and GATE tools. For easier checking of results,
subfields like telecommunications law or state aid law were selected. The output was
then used for extension and enlargement of the knowledge representation.
The work is still ongoing but some preliminary remarks can be made. The output
improves very much the ontological representation. Further analytical work is much
supported by ontological representation, faster browsing, reading and text extraction.
However, the workload of checking the output remains significant.
Thus, such efforts of knowledge representation may only be justified if they support
the production of other knowledge products like handbooks and commentaries. Only a
symbiosis of these efforts may produce the required “scaling-up” of legal ontologies. It
has to be noted that without the day-to-day input of legal authors the quality of
knowledge representation may not be sufficient. Later, automated applications of legal
reasoning can be also envisaged.
As a preliminary result, the problem of these methods is not its usability but its
acceptance by legal authors. Refinement of methods and improved interface will play a
decisive role and will be part of future research.
5 Conclusions and Future Work
Next steps for a dynamic electronic legal commentary require semantic indexing of
legal information systems and extraction of ontological information of these huge data
warehouses. A combination of intellectual input, NLP tools and appropriate ontological
representation may overcome the existing bottleneck of legal knowledge acquisition of
legal ontologies. Preliminary results based on the test environment of Austrian law
using the tools of SOM/GHSOM, KONTERM and GATE show the feasibility of this
method. However, refinement and adaptation still require important personal resources
in practice. Success of this method will depend on the willingness of legal writers to
modify working habits and include this approach in their methods of legal structural
analysis.
References
1. Index 2006, Rechtsprechung und Schrifttum, Jahresübersicht 2006. Band 59, Begründet von
Franz Hohenecker. Manz, Wien (2007)
2. Jurivoc. Dreisprachiger Thesaurus des Schweizerischen Bundesgerichts.
http://www.bger.ch/de/index/juridiction/jurisdiction-inherit-
template/jurisdiction-jurivoc-home.htm (2009).
3. Schweighofer, E.: Computing Law: From Legal Information Systems to Dynamic Legal
Electronic Commentaries, In: Magnusson Sjöberg, C., Wahlgren, P. (eds.), Festskrift till
Peter Seipel pp. 569-588. Norsteds Juridik AB, Stockholm (2006)
4. Berners-Lee, T. et al.: The Semantic Web. Scientific American Vol. 284, No. 5, 34-53
(2001)
5. Blair, D. C., Maron, M. E.: An Evaluation of Retrieval Effectiveness for a Full-text
Document-retrieval System. Comm ACM, Vol. 28, 289-299 (1985)
6. ISO: Documentation. Guidelines for the establishment and development of monolingual
thesauri, ISO 2788 (1986)
7. Gruber, T.R.: A Translation Approach to Portable Ontology Specifications. Knowledge
Acquisition vol. 5/2, 199-220 ( 1993)
8. Bench-Capon, T.J.M., Visser, P.R.S.: Ontologies in Legal Information Systems: The Need
for Explicit Specifications of Domain Conceptualisations. In: Proceedings of the 6th ICAIL,
pp. 132-141. ACM Press, New York, NY (1997)
9. Kralingen, R.W. van: Frame-based Conceptual Models of Staute Law. Ph.D. Thesis,
University of Leiden, The Hague (1995)
10. Visser, P.R.S.: Knowledge Specification for Multiple Legal Tasks: A Case Study of the
Interaction Problem in the Legal Domain. Computer Law Series Vol. 17, Kluwer Law
International, The Hague (1995)
11. Valente, A.: Legal knowledge engineering: A modelling approach. IOS Press, Amsterdam
(1995)
12. Breuker, J. and Hoekstra, R.: DIRECT: Ontology-based Discovery of Responsibility and
Causality in Legal Case Descriptions. In: Proceedings of the 17th JURIX. IOS Press,
Amsterdam et al. (2004)
13. Hoekstra, R., Breuker, J., De Bello, M., Boer, A.: The LKIF Core Ontology of Basic Legal
Concepts. In: Casanovas, P., Biasiotti, M. A., Francesconi, E., Sagri, M. T. (eds.)
Proceedings of LOAIT 07, II. Workshop on Legal Ontologies and Artificial Intelligence
Techniques, pp. 43-64. http://www.ittig.cnr.it/loait/LOAIT07-
Proceedings.pdf (2007)
14. Casellas, N., Casanovas, P., Vallbé, J.-J., Poblet, M., Blázquez, M., Contreras, J., López-
Cobo, J.-M., Benjamins, R.: Semantic Enhancement for Legal Information Retrieval:
IURISERVICE performance. In: Eleventh International Conference on Artificial
Intelligence and Law, pp. 49-57. ACM Press, New York (2007).
15. Dini, L., Liebwald, D., Mommers, L., Peters, W., Schweighofer, E., Voermans, W.: LOIS
Cross-lingual Legal Information Retrieval Using a WordNet Architecture. In: Proc Tenth
Int Conf on Artificial Intelligence & Law, pp. 163-167. ACM Press, New York (2005).
16. Ajani, G., Lesmo, L., Boella, G., Mazzei, A., Rossi, P.: Terminological and Ontological
Analysis of European Directives: multilinguism in Law. In: Eleventh International
Conference on Artificial Intelligence and Law, pp. 43-48. ACM Press, New York (2007).
17. Francesconi, E., Spinosa, P., Tiscorina, D.: A linguistic-ontological support for multilingual
legislative drafting: the DALOS Project. In: Casanovas, P., Biasiotti, M. A., Francesconi,
E., Sagri, M. T. (eds.) Proceedings of LOAIT 07, II. Workshop on Legal Ontologies and
Artificial Intelligence Techniques, pp. 103-112. http://www.ittig.cnr.it/
loait/LOAIT07-Proceedings.pdf (2007)
18. Lenci, A., Montemagni, S., Pirrelli, V., Ventur, G.: NLP-based ontology learning from legal
texts. A case Study, In: Casanovas, P., Biasiotti, M. A., Francesconi, E., Sagri, M. T. (eds.)
Proceedings of LOAIT 07, II. Workshop on Legal Ontologies and Artificial Intelligence
Techniques, pp. 103-112. http://www.ittig.cnr.it/loait/LOAIT07-
Proceedings.pdf (2007)
19. Schweighofer, E.: Legal Knowledge Representation, Automatic Text Analysis in Public
International and European Law. Kluwer Law International, The Hague (1999)
20. Schweighofer, E. et al.: Improvement of Vector Representation of Legal Documents with
Legal Ontologies. In: Proceedings of the 5th BIS, Poznan University of Economics Press,
Poznan (2002)
21. Moens, M.-F. et al.: Abstracting of Legal Cases: The SALOMON Experience. In:
Proceedings of the 6th ICAIL pp. 114-122. ACM Press, New York (1997)
22. Smith, J.C. et al.: Artificial Intelligence and Legal Discourse: The Flexlaw Legal Text
Management System. Artificial Intelligence and Law Vol. 3/1-2, 55-95 (1995).
23. Brüninghaus, S. and Ashley, K.D.: Improving the Representation of Legal Case Texts with
Information extraction Methods. In: Proceedings of the 8th ICAIL pp. 42-51. ACM Press,
New York (2001)
24. Gonçalves, T. and Quaresma, P.: Is linguistic information relevant for the classification of
legal texts? In: Proceedings of the 10th ICAIL, pp. 168-176. ACM Press, New York (1995)
25. AustLII website. http://www.austlii.edu.au
26. CiteSeer website. http://citeseer.ist.psu.edu/cs
27. RIS website: http://www.ris.bka.gv.at
28. GATE (General Architecture for Text Engineering) Engineering) website.
http://gate.ac.uk/