V. Richard Benjamins Pompeu Casanovas
Joost Breuker Aldo Gangemi (Eds.)
Law and the
Semantic Web
Legal Ontologies, Methodologies,
Legal Information Retrieval, and Applications
13

Series Editors
Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA
Jörg Siekmann, University of Saarland, Saarbrücken, Germany
Volume Editors
V. Richard Benjamins
Intelligent Software Components (iSOCO)
Pedro de Valdivia 10, 28006 Madrid, Spain
E-mail: rbenjamins@isoco.com
Pompeu Casanovas
Autonomous University of Barcelona, Institute of Law and Technology
UAB Sociolegal Studies Group, 08193 Cerdanyola, Barcelona, Spain
E-mail: pompeu.casanovas@uab.es
Joost Breuker
University of Amsterdam, Leibniz Center for Law
1000 BA Amsterdam, The Netherlands
E-mail: breuker@lri.jur.uva.nl
Aldo Gangemi
Laboratory for Applied Ontology
Viale Marx 15, 00137 Rome, Italy
E-mail: gangemi@ip.rm.cnr.it
Library of Congress Control Number: 2005921097
CR Subject Classification (1998): I.2, H.4, H.3, H.5, J.1, K.4.1-2
ISSN 0302-9743
ISBN 3-540-25063-8 Springer Berlin Heidelberg New York
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Table of Contents
Part I Context of the Book
Law and the Semantic Web, an Introduction
V. Richard Benjamins, Pompeu Casanovas, Joost Breuker,
Aldo Gangemi…………………………………………………………….. .… 1
Introduction: Legal Informatics and the Conceptions of the Law
Josep Aguiló-Regla…………………………………………………………… 18
Statistical Study of Judicial Practices
Ramón Álvarez, Mercedes Ayuso, Mónica Bécue…………………………. ..... 25
Part II Theoretical Papers: Legal Ontologies and Methodologies
Use and Reuse of Legal Ontologies in Knowledge Engineering and
Information Management
Joost Breuker, André Valente, Radboud Winkels…………………………. .. 36
Types and Roles of Legal Ontologies
Andre Valente…………………………………..……………………………… 65
CAUSATIONT: Modeling Causation in AI&Law
Jos Lehmann, Joost Breuker, Bob Brouwer…………………………………… .77
A Constructive Framework for Legal Ontologies
Aldo Gangemi, Maria-Teresa Sagri, Daniela Tiscornia…………………… .. 97
On the Ontological Status of Norms
Guido Boella, Leonardo Lesmo, Rossana Damiano………………………… 125
Building Legal Ontologies with METHONTOLOGY and WebODE
Oscar Corcho, Mariano Fernández-López, Asunción Gómez-Pérez,
Angel López-Cima………...…………………………………………………... 142
Institutional Pragmatics and Legal Ontology Limits of the Descriptive
Approach of Texts
Danièle Bourcier…………………………………………………………… 158

Part III Practice Papers: Information Retrieval and Applications
Using NLP Techniques to Identify Legal Ontology Components:
Concepts and Relations
Guiraude Lame…………………………………………………………….. 169
A Methodology to Create Legal Ontologies in a Logic Programming
Information Retrieval System
José Saias, Paulo Quaresma..……………………………………………... 185
Iuriservice: An Intelligent Frequently Asked Questions System to Assist
Newly Appointed Judges
V.R. Benjamins, P. Casanovas, J. Contreras, J.M. Lopez Cobo,
L. Lemus. ………………………….………………………….…………… 201
NetCase: An Intelligent System to Assist Legal Services Providers in
Transnational Legal Networks
Jesús Contreras, Marta Poblet……………………………………………… 218
No Model Behaviour: Ontologies for Fraud Detection
John Kingston, Burkhard Schafer, Wim Vandenberghe……………………… 233
Author Index…………………………………………………………………….249
XII Table of Contents

V.R. Benjamins et al. (Eds.): Law and the Semantic Web, LNCS 3369, pp. 142–157, 2005.
© Springer-Verlag Berlin Heidelberg 2005
Building Legal Ontologies with METHONTOLOGY
and WebODE
Oscar Corcho1, Mariano Fernández-López, Asunción Gómez-Pérez,
and Angel López-Cima
Facultad de Informática. Universidad Politécnica de Madrid,
Campus de Montegancedo, s/n. 28660 Boadilla del Monte. Madrid, Spain
{ocorcho, mfernandez, asun, alopez}@fi.upm.es
Abstract. This paper presents how to build an ontology in the legal domain
following the ontology development methodology METHONTOLOGY and
using the ontology engineering workbench WebODE. Both of them have been
widely used to develop ontologies in many other domains. The ontology used to
illustrate this paper has been extracted from an existing class taxonomy
proposed by Breuker, and adapted to the Spanish legal domain.
1 Introduction
When the application of the technology in a specific area attains some degree of
maturity, it stops being an art and becomes an engineering. A characteristic of an
engineering is that it provides methods, methodologies and tools to perform the tasks
required in such area. Methodologies state “what”, “who” and “when” a given activity
should be performed [7], and tools give support to such activities. Ontological
Engineering refers to the set of activities that concern the ontology development
process, the ontology life cycle, the methods and methodologies for building
ontologies, and the tool suites and languages that support them [12].
Some outstanding works on how to develop ontologies methodologically are the
following: Uschold and King’s [24], Grüninger and Fox’s [14], METHONTOLOGY
[9, 10], and On-To-Knowledge [20], among others. Concerning software platforms
that aid in ontology development, we can mention Protégé-2000 [18], OntoEdit [21],
KAON [17], and WebODE [1], among others.
In this paper we present how to develop a legal entity ontology following
METHONTOLOGY and using WebODE (the methodology and software platform
proposed by the Ontological Engineering Group at UPM). With them we have built
ontologies in different domains, like Chemistry, Science, knowledge management, e-
commerce, etc.
This paper is addressed to experts in Law who want to build ontologies in that
domain. We present how we have adapted a class taxonomy proposed by Breuker2, to
build a legal entity ontology in the context of the Spanish legal domain. We have used
1
Now at Intelligent Software Components (ocorcho@isoco.com)
2
http://zeus.ics.forth.gr/forth/ics/isl/projects/ontoweb/notes/legal-ontol-ontoweb-sard-2002.ppt

Building Legal Ontologies with METHONTOLOGY and WebODE 143
METHONTOLOGY (section 2) and WebODE (section 3) as our methodological and
technological frameworks. Section 4 describes briefly other methods and
methodologies, and tools. Finally, section 5 presents conclusions to this work.
2 Building a Legal Entity Ontology According to
METHONTOLOGY
2.1 METHONTOLOGY in a Nutshell
METHONTOLOGY [9, 10] was developed within the Ontological Engineering group
at Universidad Politécnica de Madrid. This methodology enables the construction of
ontologies at the knowledge level, and has its roots in the main activities identified by
the IEEE software development process [15] and in other knowledge engineering
methodologies [13].
ODE and WebODE [1] were built to give technological support to
METHONTOLOGY. Other ontology tools and tool suites can also be used to build
ontologies following this methodology, for example the ones mentioned in the
introduction: Protégé-2000 [18], OntoEdit [21], KAON [17], etc.
METHONTOLOGY has been proposed3 for ontology construction by the Foundation
for Intelligent Physical Agents (FIPA), which promotes inter-operability across agent-
based applications.METHONTOLOGY guides in how to carry out the whole
ontology development through the specification, the conceptualization, the
formalization, the implementation and the maintenance of the ontology (see figure 1).
We now describe briefly what each activity consists in:
- The specification activity states why the ontology is being built, what its intended
uses are and who the end-users are.
- The conceptualization activity in METHONTOLOGY organizes and converts an
informally perceived view of a domain into a semi-formal specification using a
set of intermediate representations (IRs) based on tabular and graph notations that
can be understood by domain experts and ontology developers. The result of the
conceptualization activity is the ontology conceptual model. The formalization
activity transforms the conceptual model into a formal or semi-computable
model. The implementation activity builds computable models in an ontology
language (Ontolingua [8], RDF Schema [4], OWL [5], etc.). Tools implement
automatically conceptual models in varied ontology languages. For example,
WebODE imports and exports ontologies from and to the following languages:
XML, RDF(S), OIL, DAML+OIL, OWL, CARIN, FLogic, Jess, and Prolog.
- The maintenance activity updates and corrects the ontology if needed.
METHONTOLOGY also identifies management activities (schedule, control, and
quality assurance), and support activities (knowledge acquisition, integration,
evaluation, documentation, and configuration management).
3
http://www.fipa.org/specs/fipa00086/

144 O. Corcho et al.
Fig. 1. Activities in the ontology development proposed by METHONTOLOGY
In the next sections we show the process followed to conceptualize an ontology
about legal entities (juridical persons, organizations, etc.) in the Spanish legal domain.
As already commented above, we have adapted a class taxonomy of legal entities
proposed by Breuker for the Spanish legal domain. The definitions provided for some
of the legal terms of this ontology are adapted from the LEGAMedia lexicon4.
2.2 Main Ontology Modelling Components
METHONTOLOGY proposes to conceptualize ontologies with a set of tabular and
graphical IRs. Such IRs allow modeling the components described in this section.
Concepts are taken in a broad sense. For instance, in the legal domain, concepts are:
juridical person, court, juvenile, etc. Concepts in the ontology are
usually organized in taxonomies through which inheritance mechanisms can be
applied. For instance, we can represent a taxonomy of legal entities (which
distinguishes persons and organizations), where a juridical person is a subclass
of a person, a company is a subclass of a juridical person, a private
company is a subclass of company, etc.
Relations represent a type of association between concepts of the domain. If the
relation links two concepts, for example, hears, which links court to lawsuit, it
is called binary relation. An important binary relation is Subclass-Of, which is used
for building the class taxonomy, as shown above. Each binary relation may have an
inverse relation that links the concepts in the opposite direction. For example, the
relation is heard is the inverse of hears.
4
http://www.legamedia.net/lx/lx.php

Building Legal Ontologies with METHONTOLOGY and WebODE 145
Instances are used to represent elements or individuals in an ontology. An example of
instance of the concept Court is Albacete Provincial Court or
Constitutional Court. Relations can be also instantiated. For example, we
can express that Albacete Provicial Court hears 127/2004 lawsuit as follows: hears
(Albacete Provincial Court, 127/2004 lawsuit), using a first order
logic notation.
Constants are numeric values that do not change during much time. For example,
adult age in Spain.
Attributes describe properties of instances and of concepts. We can distinguish two
types of attributes: instance and class attributes. Instance attributes describe concept
instances, where they take their values. These attributes are defined in a concept and
inherited by its subconcepts and instances. For example, the first name of a
physical person is proper to each instance. Class attributes describe concepts
and take their values in the concept where they are defined. Class attributes are
neither inherited by the subclasses nor by the instances. An example is the attribute
type of control of the concept company, which can be used to determine the
type of control of a private company, a public company, and a shared
control company. Ontology development tools usually provide predefined
domain-independent class attributes for all the concepts, such as the concept
documentation, synonyms, acronyms, etc. Besides, other user-defined domain-
dependent class attributes can be usually created.
Formal axioms are logical expressions that are always true and are normally used to
specify constraints in the ontology. An example of axiom is that a person cannot be
the defendant and the plaintiff in the same lawsuit.
Rules are generally used to infer knowledge in the ontology, such as attribute values,
relation instances, etc. An example of rule is that lawsuits where juveniles up 14 years
old are the defendants are heard by a juvenile court.
2.3 Conceptualization of a Legal Entity Ontology
When building ontologies, ontologists should not be anarchic in the use of the above
modeling components during the ontology conceptualization. They should not define,
for instance, a relation if the linked concepts are not precisely defined in the ontology.
METHONTOLOGY includes in the conceptualization activity the set of structuring
knowledge tasks shown in figure 2.
The figure emphasizes the ontology components (concepts, attributes, relations,
constants, formal axioms, rules, and instances) built inside each task, and illustrates
the order proposed to create such components during the conceptualization activity.
This modeling process is not sequential, though some order must be followed to
ensure the consistency and completeness of the knowledge represented. If new
vocabulary is introduced, the ontologist can return to any previous task.
Task 1: To build the glossary of terms. First, the ontologist builds a glossary of
terms that includes all the relevant terms of the domain (concepts, instances,
attributes, relations between concepts, etc.), their natural language descriptions, and

146 O. Corcho et al.
Fig. 2. Tasks of the conceptualization activity according to METHONTOLOGY
Table 1. An excerpt of the Glossary of Terms of the legal entity ontology
Name Synonyms Acronyms Description Type
adult age in Spain -- -- The adult age in Spain is 18 Constant
Court juridical tribunal --
Although 'court' can be understood as a physical place or as
a judge, we assume (in this ontology) that a court is a
judicial tribunal
Concept
birth day -- -- The day when a person was born Instance Attribute
is defendant(person,
lawsuit) -- -- It is the lawsuit of a defendant Relation
their synonyms and acronyms. Table 1 illustrates a section of the glossary of terms of
the legal entity ontology. It is important to mention that on the initial stages of the
ontology conceptualization the glossary of terms might contain several terms that
refer to the same compoment. Then the ontologist should detect that they appear as
synonyms.
Task 2: To build concept taxonomies. When the glossary of terms contains a sizable
number of terms, the ontologist builds concept taxonomies to define the concept
hierarchy.
To build concept taxonomies, the ontologist selects terms that are concepts from
the glossary of terms. METHONTOLOGY proposes to use the four taxonomic
relations defined in the Frame Ontology [8] and the OKBC Ontology [5]: Subclass-
Of, Disjoint-Decomposition, Exhaustive-Decomposition, and Partition.
A concept C1 is a Subclass-Of another concept C2 if and only if every instance of
C1 is also an instance of C2. For example, as Fig. 3 illustrates, physical person is
a subclass of person, since every physical person is a person. A concept can be a
subclass of more than one concept in the taxonomy. For instance, the concept
shared control company is a subclass of the concepts private company
and public company, since a shared control company is controlled by private and
public entities.

Building Legal Ontologies with METHONTOLOGY and WebODE 147
A Disjoint-Decomposition of a concept C is a set of subclasses of C that do not
have common instances and do not cover C, that is, there can be instances of the
concept C that are not instances of any of the concepts in the decomposition. For
example (see Fig. 3), the concepts ministry and court make up a disjoint
decomposition of the concept organization because no organization can be
simultaneously a ministry, and a court. Besides, there may be instances of the concept
organization that are not instances of any of the two classes.
An Exhaustive-Decomposition of a concept C is a set of subclasses of C that cover
C and may have common instances and subclasses, that is, there cannot be instances
of the concept C that are not instances of at least one of the concepts in the
decomposition. For example (see Fig. 3), the concepts private company and
public company make up an exhaustive decomposition of the concept company
because there are no companies that are not instances of at least one of those
concepts, and those concepts can have common instances. For example, a shared
control company is a public company and a private company.
Fig. 3. An excerpt of the Concept Taxonomy of the legal entity ontology
A Partition of a concept C is a set of subclasses of C that do not share common
instances and that cover C, that is, there are not instances of C that are not instances of
one of the concepts in the partition. For example, Fig. 3 shows that the concepts
juvenile and person legally of age make up a partition of the concept
physical person because every physical is either juvenile or person legally
of age.
Once the ontologist has structured the concepts in the concept taxonomy, and
before going ahead with the specification of new knowledge, s(he) should examine
that the taxonomies contain no errors [11]. For example, it should be checked that an

148 O. Corcho et al.
element is not simultaneously instance of two classes of a disjoint decomposition,
that there are not loops in the concept taxonomy, that several terms do not refer to the
same concept, etc.
Task 3: To build ad hoc binary relation diagrams. Once the taxonomy has been
built and evaluated, the conceptualization activity proposes to build ad hoc binary
relation diagrams. The goal of this diagram is to establish ad hoc relationships
between concepts of the same (or different) concept taxonomy. Figure 4 presents a
fragment of the ad hoc binary relation diagram of our legal entity ontology, with the
relations is plaintiff, is defendant and hears, and their inverses has
plaintiff, has defendant and is heard. Such relations connect the root
concepts (person and lawsuit, and court and lawsuit) of the concept
taxonomies of legal entities and lawsuits. From an ontology integration perspective,
such ad hoc relations express that the legal entity ontology will include the lawsuit
ontology and vice versa.
Before going ahead with the specification of new knowledge, the ontologist
should check that the ad hoc binary diagrams have no errors. The ontologist should
figure out whether the domains and ranges of each argument of each relation delimit
exactly and precisely the classes that are appropriate for the relation. Errors appear
when the domains and ranges are imprecise or over-specified.
Fig. 4. An excerpt of the Diagram of ad hoc Binary Relations of the legal entity ontology
Task 4: To build the concept dictionary. Once the concept taxonomies and ad hoc
binary relation diagrams have been generated, the ontologist must specify which are
the properties and relations that describe each concept of the taxonomy in a concept
dictionary, and, optionally, their instances.
A concept dictionary contains all the domain concepts, their relations, their
instances, and their class and instance attributes. The relations specified for each
concept are those whose domain is the concept. For example, the concept person
has two relations: is plaintiff and is defendant. Relations, instance
attributes and class attributes are local to concepts, which means that their names can
be repeated in different concepts. Table 2 shows a small section of the concept
dictionary of the legal entity ontology.

Building Legal Ontologies with METHONTOLOGY and WebODE 149
Table 2. An excerpt of the Concept Dictionary of the legal entity ontology
Concept name Instances Class attributes Instance attributes Relations
Court
Constitutional Court
National Court
Supreme Court
Albacete Provincial Court
--
number of members
seat
territorial jurisdiction
hears
Company -- type of control name --
Lawsuit -- -- --
has defendant
has plaintiff
is heard
Person -- -- -- is defendant is plaintiff
physical person -- --
age
birth day
death day
first family name
first name
nationality
second family name
is mother of
has father
has mother
is father of
As we said before, once the concept dictionary has been built, the ontologist must
describe in detail each of the ad hoc binary relations, class attributes, and instance
attributes appearing in it. In addition, the ontologist must describe accurately each of
the constants that appear in the glossary of terms. Though METHONTOLOGY does
all these tasks, it does not propose a specific order to perform them.
Task 5: To define ad hoc binary relations in detail. The goal of this task is to
describe in detail all the ad hoc binary relations included in the concept dictionary,
and to produce the ad hoc binary relation table. For each ad hoc binary relation, the
ontologist must specify its name, the names of the source and target concepts, its
cardinality, and its inverse relation. Table 3 shows a section of the ad hoc binary
relation table of the legal entity ontology, which contains the definition of the
relations is defendant, is plaintiff, etc.
Table 3. An excerpt of the ad hoc Binary Relation Table of the legal entity ontology
Relation name Source concept Source cardinality (Max) Target concept Inverse relation
is defendant Person N lawsuit has defendant
is plaintiff Person N lawsuit has plaintiff
Hears Court N lawsuit is heard
has defendant Lawsuit N person is defendant
has plaintiff Lawsuit N person is plaintiff
is heard Lawsuit N court hears
Task 6: To define instance attributes in detail. The aim of this task is to describe in
detail all the instance attributes already included in the concept dictionary by means
of an instance attribute table. Each row of the instance attribute table contains the
detailed description of an instance attribute. Instance attributes are those attributes

150 O. Corcho et al.
that describe the instances of the concept and whose value(s) may be different for
each instance of the concept. For each instance attribute, the ontologist must specify
the following fields: its name; the concept it belongs to (attributes are local to
concepts); its value type; and range of values (in the case of numerical values);
minimum and maximum cardinality; instance attributes, class attributes and constants
used to infer values of the attribute; attributes that can be inferred using values of this
attribute; formulae or rules that allow inferring values of the attribute; and references
used to define the attribute. Table 4 shows a fragment of the instance attribute table of
the legal entity ontology. Some of the previous fields are not shown for the sake of
space. This table contains some of the instance attributes of the concept court:
number of members, seat, and territorial jurisdiction.
The use of measurement units in numerical attributes causes the integration of the
Standard Units ontology. This is an example of how METHONTOLOGY proposes to
integrate ontologies during the conceptualization activity, and not to postpone the
integration to the ontology implementation activity.
Table 4. An excerpt of the Instance Attribute Table of the legal entity ontology
Instance attribute name Concept name Value type Value Range Cardinality
number of members court Integer 1 .. (1, 1)
seat court String -- (1, 1)
territorial jurisdiction court String -- (1, 1)
Task 7: To define class attributes in detail. The aim of this task is to describe in
detail all the class attributes already included in the concept dictionary by means of a
class attribute table. Each row of the class attribute table contains a detailed
description of the class attribute. For each class attribute, the ontologist should fill the
following information: name; the name of the concept where the attribute is defined;
value type; value(s); cardinality; the instance attributes whose values can be inferred
with the value of this class attribute; etc. For example, the class attribute type of
control would be defined for the concepts private company and public company as
presented in Table 5.
Table 5. An excerpt of the Class Attribute Table of the legal entity ontology
Class attribute name Defined concept Value type Cardinality Values
type of control private company [private,public] (1,2) private
type of control public company [private,public] (1,2) public
Task 8: To define constants in detail. The aim of this task is to describe in detail
each of the constants defined in the glossary of terms. Each row of the constant table
contains a detailed description of a constant. For each constant, the ontologist must
specify the following: name, value type (a number, a mass, etc.), value, the
measurement unit for numerical constants, and the attributes that can be inferred using

Building Legal Ontologies with METHONTOLOGY and WebODE 151
the constant. Table 6 shows a fragment of the constant table of our legal entity
ontology, where the constant adult age in Spain is defined. The attributes that
can be inferred with the constant are omitted.
Table 6. An excerpt of the Constant Table of the legal entity ontology
Name Value type Value Measurement unit
adult age in Spain Cardinal 18 year
METHONTOLOGY proposes to describe formal axioms and rules in parallel once
concepts and their taxonomies, ad hoc relations, attributes, and constants have been
defined.
Task 9: To define formal axioms. To perform this task, the ontologist must identify
the formal axioms needed in the ontology and describe them precisely. For each
formal axiom definition, METHONTOLOGY proposes to specify the following
information: name, NL description, the logical expression that formally describes the
axiom using first order logic, the concepts, attributes and ad hoc relations to which the
axiom refers, and the variables used.
Table 7. An excerpt of the Formal Axiom Table of the lawsuit ontology
Axiom name Description Expression Referred
concepts
Referred
relations Variables
incompatibility plaintiff
defendant
A person cannot be
plaintiff and
defendant in the
same lawsuit
not (exists(?X,?Y)
(person(?X) and
lawsuit(?Y) and
[is plaintiff](?X,?Y) and
[is defendant](?X,?Y)))
person
lawsuit
is plaintiff
is defendant
?X
?Y
As we have already commented, METHONTOLOGY proposes to express formal
axioms in first order logic. Table 7 shows a formal axiom in our legal entity ontology
that states that “A person cannot be plaintiff and defendant in the same lawsuit”. The
columns that correspond to the referred concepts and relations contain the concepts
and relations that are used inside the formal axiom. The variables used are ?X for
person, and ?Y for the lawsuit.
We must note that the definition of the logical expression may be difficult for an
expert with no experience in first order logic.
Task 10: To define rules. Similarly to the previous task, the ontologist must identify
first which rules are needed in the ontology, and then describe them in the rule table.
For each rule definition, METHONTOLOGY proposes to include the following
information: name, NL description, the expression that formally describes the rule,
the concepts, attributes and relations to which the rule refers, and the variables used in
the expression.

152 O. Corcho et al.
METHONTOLOGY proposes to specify rule expressions using the template if
<conditions> then <consequent>. The left-hand side of the rule consists of
conjunctions of atoms, while the right-hand side of the rule is a single atom.
Table 8 shows a rule that states and establishes that “Lawsuits where juveniles up
14 years old are defendants are heard by a juvenile court”. This rule would let us infer
the type of court for juveniles. As shown in the table, the rule refers to the concepts
juvenile, lawsuit and court, to the attribute age, and to the relations is
defendant and hears. The variables used are ?X for the juvenile, ?Y for the
integer, lawsuit for ?Z and ?Z for court.
As in the case of formal axioms, the definition of the rule expression may be
difficult for experts who have little experience in first order logic.
Table 8. An excerpt of the Rule Table of the legal entity ontology
Rule name Description Expression Concepts Referred
attributes
Referred
relations Variables
juvenile
courts for
juveniles
Lawsuits where
juveniles up 14
years old are
defendants are
heard by a juvenile
court
If juvenile(?X) and
lawsuit(?Z) and
court(?W) and
age(?X, ?Y) and
?Y > 14 and
[is defendant](?X, ?Z) and
hears(?W, ?Z)
then [juvenile court](?W)]
juvenile
lawsuit
court
age is defendant Hears
?X
?Z
?W
Task 11: To define instances. Once the conceptual model of the ontology has been
created the ontologist might define relevant instances that appear in the concept
dictionary inside an instance table. For each instance, the ontologist should define: its
name, the name of the concept it belongs to, and its attribute values, if known. Table 9
presents some instances of the instance table of our legal entity ontology: National
Court, Supreme Court and Constitutional Court). All of them are
instances of the concept court, as defined in the concept dictionary, and they have
some attribute and relation values specified, for: seat, territorial
jurisdiction, and number of members. These instances could have more
than one value for the attributes whose maximum cardinality is higher than one.
Table 9. An excerpt of the Instance Table of the legal entity ontology
Instance name Concept name Attribute Values
seat Madrid National Court court
territorial jurisdiction Spain
Supreme Court court territorial jurisdiction Spain
number of members 12
Constitutional Court court
territorial jurisdiction Spain
METHONTOLOGY has been used by different groups to build ontologies on
Chemistry, Science, knowledge management, e-commerce, etc. A detailed description
of this ontology building methodology can be found in [12].

Building Legal Ontologies with METHONTOLOGY and WebODE 153
3 Building a Legal Ontology with WebODE
WebODE5 [1] is an ontological engineering workbench developed by the Ontological
Engineering group at Universidad Politécnica de Madrid (UPM). The current version
is 2.0. WebODE is the offspring of the ontology design environment ODE, a
standalone ontology tool based on tables and graphs, which allowed users to
customize the knowledge model used for conceptualizing their ontologies according
to their KR needs. Both ODE and WebODE give support to the ontology building
methodology METHONTOLOGY, described in the previous section.
Currently, WebODE contains an ontology editor, which integrates most of the
ontology services offered by the workbench, an ontology-based knowledge
management system (ODEKM), an automatic Semantic Web portal generator
(ODESeW), a Web resources annotation tool (ODEAnnotate), and a Semantic Web
services editing tool (ODESWS). A detailed description of all of them can be found in
[12].
Let us start describing the WebODE ontology editor. The editor is a Web
application built on top of the ontology access service (ODE API), which integrates
several ontology building services from the workbench: ontology edition, navigation,
documentation, merge, reasoning, etc.
Three user interfaces are combined in this ontology editor: an HTML form-based
editor for editing all ontology terms except axioms and rules; a graphical user
interface, called ODEDesigner, for editing concept taxonomies and relations
graphically; and WAB (WebODE Axiom Builder), for editing formal axioms and
rules. We now describe them and highlight their most important features.
Figure 5 shows a screenshot of the HTML interface for editing instance attributes
of the concept physical person of our legal entity ontology. The main areas of
this interface are:
• The browsing area. To navigate through the whole ontology and to create new
elements and modify or delete the existing ones.
• The clipboard. To easily copy and paste information between forms, so that
similar ontology components can be created easily.
• The edition area. To insert, delete and update ontology terms (concepts,
attributes, relations, etc.) with HTML forms, and tables with knowledge about
existing terms. Figure 5 shows the attributes defined for the concept physical
person: age, birthday, deathday, first family name, etc.
ODEDesigner eases the construction of concept taxonomies and ad hoc binary
relations between concepts, and allows defining views to highlight or customize the
visualization of fragments of the ontology for different users.
Concept taxonomies are created with the following set of predefined relations:
Subclass-Of, Disjoint-Decomposition, Exhaustive-Decomposition, Partition,
Transitive-Part-Of and Intransitive-Part-Of. Figures 3 and 4 show different views of
our legal entity ontology in ODEDesigner.
5
http://webode.dia.fi.upm.es/

154 O. Corcho et al.
Fig. 5. Edition of an instance attribute with the WebODE ontology editor
The WebODE Axiom Builder (WAB) is a graphical editor for creating formal
axioms and rules, like the ones presented in table 7 and table 8. This editor aims at
facilitating the creation of such components by domain experts who have not much
experience with modelling in first order logic.
We now describe other ontology building services integrated in the ontology
editor: the documentation service, ODEMerge, and the evaluation service. There are
many other WebODE services (e.g. the OKBC-based Prolog inference engine,
ODEClean, the ontology translation services, etc.) that will not be presented, since we
think that they will not be specially useful for the readers for whom this paper is
focused on.
The WebODE ontology documentation service generates WebODE ontologies in
different formats that can be used to provide their documentation: HTML tables
representing the METHONTOLOGY’s intermediate representations described in
section 2 and HTML concept taxonomies. In fact, the figures presented in such a
section are part of WebODE screenshots.
The WebODE merge service (ODEMerge) performs a supervised merge of
concepts, attributes, and ad hoc binary relations from two ontologies built for the
same domain. It uses natural language resources to find the mappings between the
components of both ontologies so as to generate the resulting merged ontology.
Finally, the WebODE workbench also provides the following ontology evaluation
functions: the ontology consistency service and the RDF(S), DAML+OIL, and OWL
evaluation services.
Edition areaBrowsing area
Clipboard

Building Legal Ontologies with METHONTOLOGY and WebODE 155
The ontology consistency service provides constraint checking capabilities for the
WebODE ontologies and is used by the ontology editor during the ontology building
process. It checks type constraints, numerical values constraints, and cardinality
constraints, and verifies concept taxonomies (i.e., external instances of an exhaustive
decomposition, loops, etc.).
The RDF(S), DAML+OIL, and OWL evaluation services evaluate ontologies
according to the evaluation criteria identified by Gómez-Pérez [11]. They detect
errors in ontologies implemented in these languages and provide suggestions about
better design criteria for them.
4 Other Methods and Tools for Ontology Development
Basically, a series of methods and methodologies for developing ontologies have
been reported in literature. In 1990, Lenat and Guha [16] published some general
steps and some interesting points about the Cyc development. Some years later, in
1995, on the basis of the experience gathered in developing the Enterprise Ontology
[23] and the TOVE (TOronto Virtual Enterprise) project ontology [14] both in the
domain of enterprise modeling, the first guidelines were proposed and later refined
in [23].
At the 12th European Conference for Artificial Intelligence (ECAI’96), Bernaras
and colleagues [3] presented a method to build an ontology in the domain of electrical
networks as part of the Esprit KACTUS project. The METHONTOLOGY
methodology appeared simultaneously and was extended in further papers [9, 10]. In
1997, a new method was proposed for building ontologies based on the SENSUS
ontology [22]. Then some years later, the On-To-Knowledge methodology appeared
within the project with the same name [20].
Concerning ontology tools’ technology, it has improved enormously since the
creation of the first environments. If we take into consideration the evolution of
ontology development tools since they appeared in the mid-1990s, we can distinguish
two groups6:
• Tools whose knowledge model maps directly to an ontology language. These
tools were developed as ontology editors for a specific language. In this group we
include: the Ontolingua Server [8], which supports ontology construction with
Ontolingua and KIF; OntoSaurus [22] with Loom; and OilEd [2] with OIL first,
later with DAML+OIL, and now with OWL.
• Integrated tool suites whose main characteristic is that they have an extensible
architecture, and whose knowledge model is usually independent of an
ontology language. These tools provide a core set of ontology related services
and are easily extended with other modules to provide more functions. In this
group we can include Protégé-2000 [18], WebODE [1], OntoEdit [21], and
KAON [17].
6
In each group, we have followed a chronological order of appearance.

156 O. Corcho et al.
5 Conclusions
In this paper, we have shown how experts on the legal domain can develop their own
ontologies following the ontology building methodology METHONTOLOGY and
using the ontology engineering workbench WebODE. This methodology and tool
have been successfully used by different groups for the development of ontologies in
diverse domains. To illustrate how to use them, we have provided an example of how
to develop an ontology about legal entities in Spain, adapting a taxonomy of legal
entities elaborated by Breuker.
The main conclusion that we can transmit to the reader is that the broad
experience on knowledge representation is not a necessary condition to build an
ontology. Experts on the legal domain can have the initiative in the development of
ontologies of their field with punctual help of experts on knowledge engineering.
METHONTOLOGY allows modelling ontologies through graphical and tabular
intermediate representations that can be understood by experts in one domain who are
not deeply involved in the ontology field. Moreover, WebODE is a software platform
that provides support to METHONTOLOGY, although it does not force to follow
such methodology.
Finally, in section 4 we have presented other methods and tools so that readers can
have the possibility of working according to other proposals.
Acknowledgments
This work has been supported by the project Esperonto (IST-2001-34373).
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