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Data & Knowledge Engineering 46 (2003) 345–375

Conceptual framework for document semantic modelling:an application to document and knowledge management

in the legal domain

D. Jouve a,b,*, Y. Amghar a, B. Chabbat b, J.-M. Pinon a

a Laboratoire d�Ing�eenierie des Syst�eemes d�Information (LIRIS), Insa de Lyon, 20 Avenue Albert Einstein,69621 Villeubanne Cedex, France

b Centre National d�Etudes et de D�eeveloppements Informatiques, Cnaf de Lyon, Tour du Cr�eedit Lyonnais,69326 Lyon Cedex 03, France

Received 12 June 2002; received in revised form 20 November 2002; accepted 29 January 2003

Abstract

The main contribution of this paper is to lay down a conceptual framework for document semantics

modeling. This framework provides a generic graphical knowledge representation model based on Sowa�sconceptual structures. Modeling primitives are introduced to represent factual and ontological knowledge

that can be expressed in electronic documents. Binding features are proposed so as to keep knowledge

representation and knowledge formulation linked together.

This framework may be applied to various domains and may accept, for this purpose, many different

ontological extensions. Thus an extension is provided so as to properly handle the particular kind ofknowledge encountered in the legal domain.

� 2003 Elsevier B.V. All rights reserved.

Keywords: Document semantic modeling; Graphical knowledge representation; Legal document

1. Introduction

The work reported in this paper has been carried out in the context of the Caisse Nationaledes Allocations Familiales which is a branch of the French National Health Care system. The

* Corresponding author. Address: Laboratoire d�Ing�eenierie des Syst�eemes d�Information (LIRIS), Insa de Lyon, 20

Avenue Albert Einstein, 69621 Villeubanne Cedex, France. Tel.: +33-472437924.

E-mail address: david.jouve@lisi.insa-lyon.fr (D. Jouve).

0169-023X/03/$ - see front matter � 2003 Elsevier B.V. All rights reserved.

doi:10.1016/S0169-023X(03)00046-6

346 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

legislation of this domain is particularly complex, and the organisation has to deal with a massiveamount of legal documents.

Much work has been done in AI and law and legal document management research areas: de-ontic logic [1,2], ontologies [3–5], legal argumentation [6], indexing and information research [7–9],neuronal or fuzzy representations and processing [10], etc. However, little attention had been paidto problems induced by regulation changes in managing legal corpus�s coherence and conformity.

The main contribution of our work is to lay down a conceptual framework for documentsemantics modeling. It has been designed to be user-friendly, reusable, extensible and easilycomputable. From this basis, an ontological extension is provided so as to properly handle theparticular kind of knowledge encountered in the legal domain. This extension derives from vanKralingen�s ontology of law [4] and provides the basics for many knowledge-based managementtools for legal documents: coherence management, impact study, conceptual retrieval, etc.

However, this framework is not restricted to the legal domain. It contains, among other ele-ments, a generic part that can be either directly reused or extended so as to represent specifics ofvarious domains of interest.

The outline of the article is as follows. In Section 2, we begin with a short description of legaldocument-related problematics. Next, an overview of the state of the art is provided in Section 3.In Section 4, we introduce a set of modeling primitives that can be used to model several kinds ofknowledge. Then, in Section 5, this set is extended so as to handle legal knowledge in a suitableway. In Section 6 we address the problem of relating knowledge representation to sources thatprovide a natural language formulation of this knowledge. In Section 7, we briefly report ourexperience in implementing the framework. Finally, we conclude in Section 8 with an assessmentof our contribution.

2. Motivations

2.1. Basic notions about legal documents

In this paper, legal documents are addressed as textual documents that express a regulationbound to a legislation, regardless of their legal weight. Statute books, decrees, ministerial circu-lars, etc. and texts that carry less legal weight (for instance, internal reference texts of some in-stitutions that operate from regulation) are different kinds of legal documents.

Legal documents play an important part in all activities related to the legal domain. In par-ticular, they constitute an efficient human communication tool to convey legal knowledge. Somespecifics differentiate them out from other kinds of documents [11]; the most important are:

(1) For any legal sub-domain, the set of relevant legal documents is partially ordered. They can belaid out into a pyramidal structure according to their legal weight. Usually, the more legalweight a document carries, the larger field of application it covers and the less precise arethe rules. For instance, in Fig. 1, the constitution is located at the highest level of the hierar-chy: it carries more legal weight than any other legal document––i.e. they must to be in accor-dance with the constitution.

Legal weight Kind of documents Origin

Constitution

DecreeMinisterial circularInternal reference textTechnical instructions

StatuteParliamentNational assembly Council of ministersGovernement dept. Organisation level 1Organisation level 2

Fig. 1. Legal documents hierarchy.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 347

(2) Legal documents have normative semantics. Norms are the most important element of legalsystems (see, for instance, [1,12–16]). A norm ‘‘is a statement to the effect that somethingought to, ought not to, may or can be done’’ [1,15]. Communicating norms means they areformulated in natural language [17]. Legal documents reflect the normative knowledge theyare meant to express.

(3) Frequently, legal documents that belong to a same legal sub-domain express similar informa-tion. Particularly, legal documents often give precise details about higher ones in the hierarchy(see Fig. 1). Typically, numerous distinct document fragments may formulate a common pieceof knowledge at different specific levels.

(4) The meaning of legal documents cannot be determined with certainty. The principal reasonsare: the use of vague, evaluatively-open and open-texture terms, and the occurrence of ambi-guities, inconsistencies and incompleteness [4].

(5) Legal documents are perpetually prone to changes. Legislation is constantly added, deletedand modified. As an example, the French Family Benefits Administration has to deal withan average of two modifications per month.

2.2. Problematics related to legal documents

Specifics listed above raise sets of problems, for instance:Legal corpus maintenance: Specifics (1), (3) and (5) lead institutions that operate from regula-

tions to spend effort to continually maintain coherence and conformity in their legal workingdocumentation [18].Normative conflict: Due to aspects (2) and (4), conflicts may arise between some valid rules

expressed in incoherent textual representations [12].Legal document processing and retrieval: Specifics (1), (3) and (4) involve different types of texts

to be related and combined in an effective way during legal problem solving tasks. This phe-nomenon is described in [7] as legal rule fragmentation.

Like numerous authors, we are convinced that many problems related to legal documents canbe handled using conceptual approaches. In this paper, we propose a framework for legal docu-ment semantic modeling. Our preoccupation can be summarised by three simple questions: how torepresent knowledge expressed in legal document, how to display it so that humans can easilyinteract with the machine, and how to relate modeled knowledge and legal sources. Our mainobjective is to improve legal information systems� efficiency and user-friendliness, and to makesuch systems easier to build.

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2.3. Knowledge, knowledge formulation and knowledge representation

Knowledge, knowledge formulation and knowledge representation are three distinct entities thatshould not be confused. What is knowledge? What is knowledge representation? Addressing ina complete manner these issues would require an extended discussion. 1 In this article, thosequestions remain open and only generalities are studied. However, the main distinctions observedare briefly sketched below.

Ayn Rand defines knowledge as ‘‘a mental grasp of a fact(s) of reality, reached either byperceptual observation or by a process of reason based on perceptual observation’’ [20].Knowledge is an abstract and elusive notion that may encompass complex information such asprocesses, actions, causality, time, motivations, hypothesis, goals, etc.

Knowledge representation is a tangible substitute for knowledge itself. Intelligent entities(programs or persons) need to use such surrogates when expressing, communicating or reasoningabout knowledge [19]. For instance, knowledge representations enable programs to reason in-ternally about things that exist externally and humans to express things about the world. Bothnatural and artificial languages can be used in order to represent knowledge. However, we dis-tinguish between the two kinds of resulting representations:

• We term knowledge formulations the representations given in natural languages. As an example,legal documents are a kind of legal knowledge formulation: they contain signs that announcenorms to the subjects of the law.

• Representation given in artificial (formal) languages are just called knowledge representations.Typically, knowledge is stored in a program in this way. For instance, the formal representationof legal norms stored into a legal knowledge base would be a kind of knowledge representation.

Computing machines can hardly reason directly from knowledge formulation. Knowledge has tobe expressed in a language that machines can ‘‘understand’’ (knowledge representation). Knowl-edge, knowledge formulation and knowledge representation are not identical. In any domain ofinterest, some pieces of knowledge are neither formulated in documents nor represented in anyknowledge base. Moreover, parts of knowledge that can be read from those formulations may notisomorphically match the ones that can be explicitly represented in knowledge bases. Commonsense as well as other knowledge has to be called upon to supplement what can be read fromknowledge formulation, but this other knowledge is seldom directly available from it.

Although formulations and explicit representations of a piece of knowledge are not strictlyisomorphic, they can be related so as to keep track of where the information used for modeling acertain element of knowledge came from. As an illustration, this feature can be applied in the legaldomain in order to automate the detection of incoherent textual fragments in a collection of legaldocuments. Conflicting rules may be detected at the knowledge representation level. Then theformulation/representation mapping may be used so as to identify the textual fragments thatformulate conflicting rules.

1 Such a discussion about ‘‘What is a knowledge representation?’’ is available in [19].

Table 1

An example of a concept frame

Element Value

Concept Claimant

Type Definition

Scope PF

Conditions PhysicalPersonðaÞ ^ FranceðbÞ ^ ActivityðcÞ ^ FrenchðeÞ^EuropeanUnionðf Þ ^ ChildðgÞ ^ Nationalityða; eÞ^Practiceða; cÞ ^ Locationðc; bÞ ^ Relativeða; gÞ ^ ResidesInðg; f Þ

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 349

3. State of the art

3.1. Ontologies of law

Modelling knowledge that can be read from documents that belong to a particular domain ofinterest should only be done according to a proper conceptualisation of this domain.

Although much work in conceptualising the legal domain has been done [1,3–5,12,13,21,22],few ontologies 2 have been proposed in the domain of AI and law [24]. The main works are:McCarty�s LLD, Stamper�s NORMA, Valente�s functional ontology of law and van Kralingenand Visser�s frame-based ontology. These ontologies have several common features, however theydiffer from the perspective adopted by their authors. They depend on what they are made for.There is no reason to consider any of these ontologies better or worse than the others. Assessingthe adequacy of an ontology may only be done if given the purposes of a particular applicationwhich would be based on it.

3.1.1. Frame-based ontology of law

The frame-based ontology of law of van Kralingen and Visser [4,17,25–27] has been designed soas to provide a set of reusable building blocks and to capture the essence of the legal domain in theform of a limited number of modeling primitives. We have shown [28,29] that such a conceptu-alisation is particularly suitable to model normative semantics of legal documents.

The frame-based ontology divides legal knowledge into three distinct entities: norms, acts andconcept description. For each of these, the ontology defines a frame structure that lists relevantattributes. Norms are general rules, standards and principles of behaviour that subjects of law arebound to comply with. They form the most important element of the legal systems [1,12,13]. Actsrepresent the dynamic aspects which affect changes in the state of the world. Concept descriptionsdeal with the meaning of concepts found in the domain. When using knowledge representationdomain vocable, norm frames, act frames and concept frames respectively model norms, act typesand concept types. Norms are assertions defining modal rules on act types and concept types.

Table 1 is an example of such a modeling frame. It provides a description of the concept type‘‘Claimant’’: a claimant is a physical person whose nationality is French, who works in France,and has children residing in the European Union.

2 In AI domain, an explicit specification of a conceptualisation is called an ontology [23].

350 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

The purpose of van Kralingen�s approach is to determine which elements of the laws areprecisely used by lawyers. He elaborates a typology of the elements to be extracted from thelegislation and organises them into semantic units (the frames) containing first-order formulae.van Kralingen�s approach is semantics oriented and based on normative properties of the legalsources. Norms, acts and concept descriptions provide a strong semantic structuring of the legaldocuments. But some aspects remain unsolved:

(1) Frames are instantiated manually from documents. However, they only keep a single referen-tial link with them through the element Promulgation. It becomes difficult to precisely de-termine where the information used for modeling a certain element comes from.

(2) Only concepts introduced by law are defined into the model. However, modeling legalknowledge involves integrating semantic descriptions of non law-specific concepts onwhich the law-specific ones are based (see Section 3.1.2 related to Valente�s ontology oflaw).

(3) The model does not integrate some of the interesting features proposed in the knowledge rep-resentation domain such as subsumption relations.

(4) Frames, enclosing first-order formulae, present a problem for human readability. This leadsto many difficulties in building and using human/machine interaction tools such as graphicaleditors.

3.1.2. Categories of legal knowledge

Sergot wrote ‘‘If one looks at a piece of legislation, it is immediately clear that there are manypurposes for which the legislation is passed’’ [30]. According to this observation, Valente breaksdown legal knowledge into six categories, which correspond to six primitive functions of a legalsystem [5]: normative knowledge, world knowledge, responsibility knowledge, reactive knowledge,meta-legal knowledge and creative knowledge.

The function of the responsibility knowledge is to assign or limit the responsibility of an agentover a given state of affairs. Reactive knowledge specifies which reaction should be taken. Creativeknowledge is used to enable a legislator to create some entity that did not previously exist. Meta-legal knowledge organises the relative position of norms and specifies how normative conflicts aresolved.

Normative knowledge roughly corresponds to knowledge that can be modeled using vanKralingen�s norm frames. It is the central knowledge type in law. World knowledge defines areal world model, which is used as a basis to express normative and other categories ofknowledge. It describes the world that is being regulated and provides a framework to definewhat behaviour ought and ought not to be performed. Actually, since a large part of theknowledge one has about the world is based on common sense, a model of the worldknowledge would have a strong common sense flavour [31]. The existence of world knowledgeas a distinct category of legal knowledge has been frequently recognised in AI and law (see forinstance [30,32,33]).

The work reported in this paper is not restricted to the single legal domain. However, an on-tological extension is specifically proposed for the legal domain (see Section 5). It mainly focuseson the part of legal knowledge that corresponds to Valente�s normative knowledge and worldknowledge categories.

Resides in Metropolitan FrancePhysical Person : John

Fig. 2. A simple conceptual graph.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 351

3.2. Conceptual graphs

Conceptual graphs are a representation scheme proposed by Sowa [34]. They are inspired bythe existential graphs of Pierce [35] and further extended with features adopted from linguistics,psychology, philosophy and AI [34,36]. Conceptual graphs constitute an expressive logical systemdesigned for a direct mapping to and from natural languages. They benefit from the advantages oflabeled graph models [37]: allowing the representation of various kinds of knowledge and se-mantic aspects of natural languages, offering a graphical notation for human readability and agraph mathematical structure for machine computability. The original definition of Sowa hasbeen enhanced by further research notably Chein and Mugnier�s [37,38], which present morerefined and formalised versions.

Informally, conceptual graphs are labeled graphs made up of two kinds of nodes: concepts andrelations. Concept nodes (depicted as boxes in Fig. 2) represent entities, attributes, states, andevents and relation nodes (depicted as ovals in Fig. 2) show how the concepts are interconnected.Edges in conceptual graphs always connect a concept node to a relation node. Every concept orrelation has an associated type. Concepts may design particular entities (individual conceptscharacterised by a specific referent) or represent unspecified entities of a given type (genericconcepts). Fig. 2 presents a simple example of conceptual graph: ‘‘John is a physical person whoresides in metropolitan France’’.

3.2.1. Fundamental structures

Below, we briefly recall some fundamental points about conceptual graphs stated in [34,37–39].Please, refer to [34,37–39] to get complete definitions.

Support: In conceptual graph, a clear separation is made between ontological knowledge andfactual knowledge [40]. A support encodes domain ontological knowledge in a structureS ¼ ðTC; TR; r; I; sÞ where: TC is a partially ordered set of concept types, TR is a partially ordered setof relation types, I is a countable infinite set of individual markers, s is an application whichassociates each individual marker to a concept type, and r is an application associating eachrelation type to a signature. The signature of a relation type specifies the arity and the maximalconcept types this relation can link. The ith argument of rðtrÞ is denoted by riðtrÞ.Conceptual graph: A conceptual graph defined on a support S represents facts, goals and hy-

pothesis related to the domain associated to S. A simple conceptual graph is a bipartite labeledmulti-graph 3 G ¼ ðR;C;U ; lÞ, not necessarily connected. R and C respectively denote the twoclasses of relation and concept nodes, C 6¼ ;. U is the set of edges. Edges incident to each relationnode r are totally ordered. They are numbered from 1 to the arity of r. The ith neighbour of r in Gis denoted by GiðrÞ. Every node has a label defined by the application l.

3 A multi-graph is a graph in which two nodes can be linked by more than one edge.

352 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

Concept type definition: Each concept type in TC is specified either as primitive or as defined. Thedefinition of a defined concept type tc is a canonical conceptual graph G containing a particulargeneric concept node of type t0c, marked x and called head of tc. G represents the differentia of tc,and t0c represents gender of tc. A concept type definition is noted: type tcðxÞ is G.Relation type definition: Each relation type in TR is specified either as primitive or as defined. The

definition of a defined relation type tr is a canonical conceptual graph G containing n particulargeneric concept nodes marked x1; x2; . . . ; xn ðnP 1Þ called arguments of tr. A relation type defi-nition is noted: relation trðx1; x2; . . . ; xnÞ is G.

4. Graph based knowledge representation model

4.1. Introduction to the model

In this section we introduce a set of representational structures based on the conceptual graphmodel and inspired by frame representation languages. The aim is to have these structures benefitfrom both:

• Conceptual graphs expressiveness, human readability, natural language matching adequacy,etc.

• Structural aspects of frame structures. Specifically, slot is an interesting structuring notionwhich introduces an intermediate granularity level between atomic valuation elements––i.e.literals or predicates––and conceptual description elements––i.e. the frames themselves.

Slots provide an additional information structure which can be computationally processed andbe helpfully used in human/machine interactions. Actually, conceptual graphs make modeledknowledge understandable to experts with little effort [41]. However, when expressing a massiveamount of knowledge, the graphs increase in size and their comprehensibility decreases. Logicallydividing a single complex graph into several consistent annotated units can make it easier tounderstand. Experts, guided by structural annotative information, can successively focus on thedifferent parts of the graph, 4 and finally have an easier global comprehension of the modeledknowledge. Thus, integrating some structural aspects of the frames to conceptual graph allows toconstruct powerful and user-friendly human/machine interfaces such as knowledge editors.Moreover, structural information provided by slots can be processed to perform efficient graphfragment indexing.

4.2. Description of the model

Factual and ontological knowledge is modeled using representational structures. In this section,a limited set of primitive ones is introduced. Informally, they are frame based structures made upof slots. Each slot is characterised by a label and corresponds to a particular aspect of the modeled

4 Such a logical graph partition may correspond to various aspects of modeled piece of knowledge.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 353

entity. There are two kinds of slots: graph slots and literal slots. The first ones embed conceptualgraph fragments. The second ones constitute a way to annotate modeled knowledge using variouskinds of multimedia data.

These structures are not domain specific. They have been designed in a sufficiently generic wayto handle various kinds of knowledge. We will see later that some ontological extensions can bebuilt upon these basics. As an illustration, specialised modeling structures will be proposed inSection 5.4 to model legal normative knowledge.

Definition 1. Given a valid conceptual graph G, we call partition of G ¼ ðR;C;U ; lÞ the set PG ¼fg1; . . . ; g2g of all connected parts of G. PG satisfies each of the following conditions:

(1) Each graph in PG is a valid conceptual graph composed of at least one concept node: for allðRi;Ci;Ui; liÞ in PG, Ci 6¼ ;.

(2) Each non-connected concept node c in G is contained in just one graph gi ¼ ðRi;Ci;Ui; liÞ ofPG, and reciprocally: for all c in C, ð 9= ðr; cÞÞððr; cÞ 2 UÞ () ð9!giÞðgi 2 PG ^ Ui ¼ ; ^ Ci ¼fcg ^ Ri ¼ ;Þ.

(3) Each relation node r 2 R and its neighbours in G are contained together in a same graphgi 2 PG. Edges incident to r in G are kept in gi, as well as the total order defined on them:for all r in R and for all j (16 j6 arityðrÞ), there is a graph gi ¼ ðRi;Ci;Ui; liÞ in PG such thatr 2 Ri and GjðrÞ 2 Ci and GjðrÞ ¼ gi;jðrÞ.

(4) Labels in graph G are kept in sub-graphs gi of PG: for all gi in PG and for all n in Ri [ Ci,liðnÞ ¼ lðnÞ.

(5) Each node of G is contained by a graph of PG :S

gi2PGRi ¼ R,

Sgi2PG

Ci ¼ C andS

gi2PGUi ¼ U .

(6) Each node of G is contained by only one graph of PG :T

gi2PGRi ¼ ;,

Tgi2PG

Ci ¼ ; andTgi2PG

Ui ¼ ;.

For any conceptual graph G, PG constitutes the set of all its connected parts, see example in Fig.3. In the particular case where G is totally connected, then PG ¼ fGg.

Co-reference is one of the three primitive structures used to assemble conceptual graphs [36].Two concept nodes that refer to the same entity are said to be co-referent or co-identical. Co-identical concept nodes may be generic nodes related by a co-reference link or individual nodeswith the same individual maker [40]. Formally, co-identity is an equivalence relation defined onthe set of concept nodes of a graph, denoted by co-ident [40].

b C1 2aA

c E

F

D

1 2

3

1 2

B b C1 2aA

c E

F

D

1 2

3

1 2

B

G G1

G2

Fig. 3. A conceptual graph G and its partition PG ¼ fG1;G2g.

b C1 2aA B1 2

A B

d

e E

F

D

c1

2 2

1

1 2

b C1 2aA

d

e E

F

D

c

1 2

1

2 2

1

3

1 2

B

G’1

G’2

GG’

3

Fig. 4. Conceptual graph splitting using co-reference.

354 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

A concept node may be split into two co-identical nodes related by a co-reference link (co-identical split generalisation rule [40]). Using this basic graph operation, a well formed connectedconceptual graph can be transformed into a non-connected conceptual graph with co-identicalconcepts.

In Fig. 4, the conceptual graph G is split using co-reference without changing its semantics. Gand G0 share a same semantics while PG ¼ fGg and PG0 ¼ fG0

1;G02g. Several notations exist to mark

co-referent concepts. For example, co-reference links are shown as dotted lines in Fig. 4.

Definition 2. A graph valued frame defined on a support S is a 4-tuple G ¼ ðG;L;S; vÞ where:

(1) G ¼ ðR;C;U ; lÞ forms a valid conceptual graph.(2) L is a set of literal values. A literal is a constant whose value is given by its external or written

form [42]. A literal value may be an encoded data such as strings, numbers, images, etc.(3) S ¼ SL [SG is a set of slot elements. SL and SG are two subsets of S corresponding to

two distinct slot classes:(a) Slots in SL are called literal slots. Each literal slot of SL is associated with a literal value

of L via the application v. Literal slots are a way to annotate modeled knowledge usingvarious kinds of multimedia data.

(b) Slots in SG are called graph slots. Each of them may be seen as a pointer on a part of G.Formally, given the partition PG of G, each element of SG points to an element in PG. Agraph valued frame must contain at least one graph slot and cannot have more graph slotsthan connected parts in G : 16CardðSGÞ6CardðPGÞ.

(4) v : S ! PG [L is an application which maps slots to their values. v obeys:(a) v isomorphically associates each literal slot element of SL to a literal value of L;(b) v associates each graph slot elements of SG to a set of sub-graphs in PG. Each element of

PG is associated to exactly one graph slot in SG.

Graph valued frames are used to model assertional and factual knowledge. Additional struc-tures are proposed in order to model ontological knowledge. New types may be defined using thecommon Aristotle�s method by genus and differentiae. Such definitions are represented usingstructures called concept type definition frame and relation type definition frame.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 355

Definition 3. A concept type definition frame is a 3-tuple TC ¼ ðtc;G; gÞ held on a support S ¼ðTC; TR;r; I; sÞ where:

(1) tc 2 TC is a defined concept type;(2) G is a graph valued frame, defined on S, corresponding to tc�s differentiae;(3) g 2 C is a generic concept node of G that corresponds to the head or formal parameter of tc.

The type of g is necessarily a supertype of tc : tc 6 typeðgÞ.

Definition 4. A relation type definition frame is a 3-tuple TR ¼ ðtr;G; aÞ held on a support S ¼ðTC; TR;r; I; sÞ where:

(1) tr 2 TR is a defined relation type;(2) G is a graph valued frame defined on S. G provides the semantics of the relation type to be

defined;(3) a ¼ ða1; . . . ; anÞ 2 Cn corresponds to the n arguments or formal parameters of tr. a ¼

ða1; . . . ; anÞ satisfies each of the following conditions:(a) Each ai 2 C (16 i6 n) is a generic concept node of the graph G.(b) Types of formal parameters ai are necessarily equal or greater than the maximal types

specified in the signature rðtrÞ of tr: for all i (16 i6 n), typeðaiÞP riðtrÞ.

4.3. Logical interpretation

Conceptual graphs are provided with semantics of first order logic. Sowa proposes a formulaoperator /, which translates conceptual graphs into formulae in predicate calculus [34,36]. Suc-cinctly, it maps circles to predicates with each arc as one argument, and it maps concept nodes totyped variables, where the type label inside each concept box designates the type. The defaultquantifier for the variable is the existential one 9. See [34,36–39] to get more refined and moreformalised translation rules. As an example of / application:

/ð½PhysicalPerson : John� ! ðResidesInÞ ! ½MetropolitanFrance�Þ¼ ð9x : MetropolitanFranceÞðPhysicalPersonðJohnÞ ^ ResidesInðJohn; xÞÞ:

4.3.1. Graph valued frameGraph valued frame semantics is provided by conceptual graphs. Slots do not provide any

additional semantic to be translated using the / operator. Slots should be seen as structure-ori-ented meta-data. They provide additional structural information about modeled factual knowl-edge, but their own semantics is not merged and does not interfere with the underlying conceptualstructure semantics. Thus, let G ¼ ðG;L;S; vÞ be a graph valued frame, the associated formulaoperator U would just be defined as UðGÞ ¼ /ðGÞ.

4.3.2. Concept type definition frame

Concept type definition frames have been defined so as to keep their semantics identical toconcept type definitions� in the conceptual graph model. Consequently, a concept type definition

Claimant

Parameters

NationalityCondition

ActivityCondition

Physical Person : *x

Type

Activity

Physical Person : ?x French Nationality

Practise France LocationPhysical Person : ?x

Child :{*}

European Union

Reside inPhysical Person : ?x RelativeChidrenrelated

Condition

t

G

ηr

S

(s1, ς(s1))

(s2, ς(s2))

(s3, ς(s3))

(s4, ς(s4))

Fig. 5. Example of a concept type definition frame.

356 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

frame TC ¼ ðtc;G; gÞ maps to a monadic lambda expression of the form: tc ¼ ðkvg : tgÞU0ðG; g; vgÞ.tg is the type of the concept node g and vg is the variable standing for the node g in the resultingformula. The operator U0ðG; g; vgÞ is similar to UðGÞ, it only differs in the manner the concept nodeg is translated: U0ðG; g; vgÞ maps the concept node g to the variable vg. vg is the formal parameterof the resulting lambda expression. It is marked by the k prefix. As an example of such atranslation, the lambda expression associated 5 with the frame in Fig. 5, is:

5 O

map.

Sectio

itself

Claimant ¼ ðkx : PhysicalPersonÞð9s : FrenchÞð9t : FranceÞð9u : ActivityÞð9v : EuropeanUnionÞð9w : SetÞð8y : ðkz : ChildÞðz 2 wÞÞðNationalityðx; sÞ ^ practiceðx; uÞ^ Locationðu; tÞ ^ Relativeðx; yÞ ^ ResidesInðt; vÞÞ:

4.3.3. Relation type definition frame

In the same way, a relation type definition frames TR ¼ ðtr;G; aÞ maps to a n-adic lambdaexpression, where n ¼ jajP 1 is the number of formal parameters.

4.4. Reasoning with frames

As seen above, semantics of the frame structures is kept in their conceptual graph fragments. Thisconsideration has a major impact on the way to reason about these structures. In fact, the principlesthat enable to reason with them are the ones used with their underlaying conceptual graphs.

The fundamental operation is called projection [34]. A projection from a simple graph G ¼ðRG;CG;EG; lGÞ to a graph H ¼ ðRH ;CH ;EH ; lHÞ is defined in [34,40,43,44] as a mapping P fromRG to RH and from CG to CH which:

(1) preserves adjacency and local orders on edges incident to relation nodes: for each relationnode r, if the ith neighbour of r is a concept node c, then the ith neighbour of PðrÞ is PðcÞ.

(2) may decrease labels: For all x 2 RG [ CG, lGðxÞP lHðPðxÞÞ.

ne can note that some predicates in the lambda expression and some concept or relation nodes in the graph do not

This is both due to the fact that the expression has been simplified by deleting all redundant co-referent nodes (see

n 4.5), and due to the presence of the defined quantifier associated to the frame {�} (generic plural [36]) which has

been expanded and translated in first order logic.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 357

Projection defines a subsumption relation P on conceptual graphs: GPH (to be read ‘‘Gsubsumes H ’’) if there is a projection from G to H . For instance, a projection exists from G ¼½PhysicalPerson� ! ðResidesInÞ ! ½France� to H ¼ ½Claimant : John� ! ðResides-InÞ ! ½France� according to the type hierarchy given in Fig. 7. The fact that ‘‘John is a claimantresiding in France’’ necessarily entails that ‘‘There exists a physical person in France’’.

Projection has been proved to be sound with respect to deduction [34]: given two simpleconceptual graphs G and H , if GPH then /ðSÞ;/ðHÞ � /ðGÞ (� is the logical consequencesymbol in first order logic). Projection has also been proved to be complete, when conceptualgraphs are in a normal 6 form [38]: given two simple conceptual graphs G et H , if /ðSÞ; /ðHÞ �/ðGÞ then GPH .

/ðSÞ is the logical interpretation of the S support. As an illustration, supposing that the typePhysicalPerson subsume the type Claimant, then /ðSÞ contains the formula ð8xÞðClai-mantðxÞ ) PhysicalPersonðxÞÞ, and:

6 A

refere

that o7 W

label o8 Fo

/ðSÞ;/ð½Claimant : John� ! ðResidesInÞ ! ½France�Þ� /ð½PhysicalPerson� ! ðResidesInÞ ! ½France�Þ:

From an algorithmic point of view, subsumption checking is an NP-complete problem [38]. Itbecomes polynomial when G is a a conceptual graph without cycles [43]. Mugnier and Chein alsonote that there is a strong equivalence between the subsumption checking problem and theconstraint satisfaction problem, allowing direct transfer of efficient algorithms from one problemto the other [37].

4.5. Graphical representation

Computer applications using knowledge representations are not only accessible to computerscientists. Domain experts have to interact with the programs so as to capture domain knowledge,to carry out checks, to consult some results, or simply to visualise knowledge content. Thepurpose of the representational structures introduced in Section 4.2 is to make these human/machine interactions easier.

The three kinds of frames introduced in Section 4.2 have an associated graphical representa-tion. They are displayed as tables. In these tables, rows correspond to frame slots. 7 As an ex-ample, Fig. 5 presents the graphical representation of a concept type definition TC ¼ ðtc; ðG;L;S; vÞ; gÞ. This frame contains no literal slot (L ¼ ;). Graph slot labels SG ¼ S ¼ fs1; . . . ; s4gare listed on the first column, and related sub-graphs are listed on the second one.

Frame in Fig. 5 represents 8 the definition of the concept type ‘‘Claimant’’. It is defined as aphysical person whose nationality is French, who works in France, and has children living in

simple conceptual graph can be put into normal form by merging all concept nodes with the same individual

nt, provided that they have the same type. Logical semantics of the produced conceptual graph is equivalent to

f the initial one [44].

ith one exception: A row named Type is added to the displayed forms of type definition frames. It contains the

f the new defined type.

r didactic purpose, the definition of the concept type ‘‘Claimant’’ has been simplified.

358 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

European Union. The slots Nationality Condition, Activity Condition and Chil-

dren related Condition represent the major aspects of this concept type. A claimant is aphysical person for whom all of these conditions are true. For example, the Activity Con-

dition slot expresses as a constraint that each instance of this kind of physical person has towork in France.

Concept nodes [Physical Person :*x] and [Physical Person :?x] represent the sameindividual. They are said to be co-referent. Co-referent concepts are members of a same co-ref-erence set. Several notations exist to mark co-referent concepts. For example, in Fig. 4, co-referentconcepts were linked with a dotted line. In graphical representation, mainly due to table basedrepresentation�s constraints, co-referent concept nodes are marked by co-reference labels. Onemember of the co-reference set is marked with a defining label, 9 prefixed with an asterisk; all othernodes of the set are marked with a bound label, prefixed by a question mark [36]. All conceptsmarked by a bound label refer to the node marked by a defining label. Any information in thatnode may be copied to other members of the associated co-reference set. As an illustration,[Physical Person :*x] is marked by a defining label. The three [Physical Person :?x]nodes are marked with bound labels and represent the same individual as [Physical Per-

son :*x].The example provided in Fig. 5 is particularly simple. But this is not the case for all type

definitions. For instance, type definitions appearing in the legal domain are frequently given withan extreme complexity, and are not easily understandable. This is mainly due to the fact thatgenerally many different aspects are focused on legal definitions, and that each of those aspects iscarefully studied. Slots logically partition the modeled knowledge and provide additional infor-mation about it. When dealing with massive amounts of knowledge, they make this knowledgeeasier for humans to manage and understand.

The graphical representation in Fig. 5 is only calculated from information encoded into TC

structure. Other data provided by the support S can be added to the display form. For example,when displaying type definition frames, it seems useful to integrate information about the positionof the defined type in the type hierarchy TR or TC.

5. Modeling composite knowledge with graph valued frames

Sometimes, knowledge to model proves to be a composite one, i.e. it can be broken into severalconsistent categories. Distinguishing between categories requires to hold some differentiatingcriteria such as role, function, purpose, domain, etc. Every category of knowledge may be ex-plicitly modeled using proper features so as to take into consideration and/or to take advantage oftheir specifics.

Legal knowledge is a perfect example of composite knowledge. As seen in Section 3.1.2, it canbe divided into several categories. This paper essentially focuses on world knowledge, legal ter-minological knowledge and normative knowledge. The issue of representing other parts of legalknowledge is reserved for further work.

9 Remark: a concept marked with a defining label cannot be a member of any other co-reference set.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 359

World knowledge: World knowledge includes knowledge that a child is a kind of a physicalperson, France is located in the European Union, if something in France then it is also in theEuropean Union, etc. Here, world knowledge plays a similar role to the one played in Valente�sontology [5]: it defines a real world model, which is used as a basis to express other kinds of legalknowledge. However, there is one exception, legal definitions or institutions are not considered asbelonging to this category.Legal terminological knowledge: Legal terminological knowledge consists of definitions of

concept or relation types that are specifics of the modeled legal sub-domain. It includes de-scriptions of legal institutions (e.g. marriage, ownership, etc.) or legal definitions (e.g. unem-ployment, claimant, etc.). Legal terminological knowledge cannot be completely dissociated fromthe world or common-sense knowledge. Legal concept or relation types cannot be describedwithout referring to this other kind of knowledge. For instance, the legal concept type ‘‘claimant’’is defined in Fig. 5 by extending ‘‘physical person’’ (World knowledge) and referring to manytypes that are not specific to any legal sub-domain (France, Location, Child, Age, etc.).Normative knowledge: Normative knowledge is the central knowledge type in law. It is

characterised as knowledge that defines standards and principles of behaviour. It includes thedescriptions of the legal norms––i.e. statements to the effect that something ought to, may orcan be done [1]. As seen in Section 3.1, they form the most important element of the legal sys-tems. The normative knowledge is based on both world knowledge and legal terminologicalknowledge.

The method and principles used in the following to model the different categories of legalknowledge can be re-applied to model various kinds of composite knowledge.

5.1. Principles

Representational structures introduced in Section 4.2 have been designed in a sufficiently genericway to model many kinds of knowledge. For example, both normative knowledge and worldknowledge can be modeled using these structures. However, no particular aspect of normativeknowledge (such as deontic modalities, restricted ranges of application, etc., see details in Section5.4) are to be taken into consideration, and this procedure may not prove to offer suitable basesfor legal knowledge management. As seen in Section 3.1, work done in conceptualising the legaldomain [3–5,21] shows that legal knowledge can be represented using more refined ontologicalstructures.

The frame-based ontology of law [4,17,25–27] has been designed so as to provide a set of re-usable building blocks and to capture the essence of the legal domain in the form of a limitednumber of modeling primitives, see Section 3.1.1. Based on van Kralingen�s frame-based ontologyof law, an ontological extension is introduced to model specialised knowledge encountered in thelegal domain.

Fig. 6 is a synthesis of our approach. World knowledge is modeled using the three represen-tational primitives introduced in Section 4.2. Legal terminological knowledge and normativeknowledge are specialised knowledge, which are modeled using specific representational struc-tures. These have been made by extending the primitive ones according to an adequate ontologyof the legal domain. Some constraints have been fixed so as to make these new structures suitabletemplates to represent legal terminological and normative knowledge.

World Knowledge

Normative Knowledge

Representational Primitives

Legal DomainExtension

models

model

extends

depends on

van KralingenOntology of Law

derives

from

Legal Terminological Knowledge

Legal Knowledge

Fig. 6. Principle used to model stratified knowledge.

360 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

5.2. World knowledge

World knowledge indiscriminately encircles descriptions about the world that is being regulatedas well as knowledge that all people in this world are supposed to have––i.e. common senseknowledge. The role it plays here is similar to the one it plays in Valente�s ontology [5]. Since lawdeals with behaviour in the world, world knowledge becomes a basic layer that is used by theother categories in order to refer to the world being regulated.

The representational structures used to represent world knowledge are: concept type definitionframes TC used in order to represent defined concept types, relation type definition frames TR

used in order to represent defined relation types and graph valued frames G in order to representfact, rules or axioms.

At this stage, the major part of the work consists in building concept and relation type hier-archies TC and TR in the support S ¼ ðTC; TR;r; I ; sÞ. In such hierarchies, each type is a generali-sation of the ones below it and a specialisation of the ones above it. Both primitive––i.e. providedwith no definitions––and defined types are organised into TC and TR. However, highest types––i.e.the most abstract and generic types––are usually primitive ones. Fig. 7 is an example of such aconcept type hierarchy.

Person Foreigner

Child

Legal Entity

Family Benefits Admin

French

France

Activity

European Union

Spatial

AttributeEntity

Claimant

Physical Person Loan Improvement

Object Purpose

Habitation

Fig. 7. A concept types hierarchy.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 361

5.3. Legal terminological knowledge

Legal terminological knowledge consists of definitions of the typical concepts and relations thatcan be found in the modeled legal sub-domain. It includes either descriptions of legal institutionsor legal definitions. For any legal sub-domain, the associated legal terminological knowledgedepends on the World knowledge: types introduced by law cannot be defined without referring tothis other kind of knowledge.

Usually, types to be described as legal terminological knowledge are subsumed by more genericones defined as part of world knowledge. For instance, ‘‘Claimant’’ is a typical concept type of theFamily Benefits legal sub-domain. Thus, it is defined as part of legal terminological knowledge.However it is subsumed by the concept type ‘‘Physical Person’’, which is part of the worldknowledge. This consideration entails both that:

(1) legal terminological knowledge and world knowledge share the same type hierarchies TC and TR,(2) representational structures that are used to model type definitions in both categories have a

unified semantics.

To model legal terminological knowledge, we have introduced three new representationalstructures that extend the primitives TC and TR (see Section 4). Each of them stands out from itsancestor by integrating some built-in slot elements, which have been directly determined from vanKralingen�s ontology of law.

Many statutes cover highly-specialised domains. Definitions prove irrelevant outside the scopeof the statute [4]. To be able to accommodate these limitations, every frame structure used tomodel legal terminological knowledge integrates a reserved literal slot labeled Scope. Its literalvalue is a string that expresses the modeled type�s range of application. For instance, when one hasto solve problems in the Family Benefits legal sub-domain, one does not usually take into accountthe legal definitions stated in the penal code.

Legal concept type descriptions: Legal concept type descriptions are structures that extendconcept type definition frames TC. They are used to model definition of concept types introducedby law.Legal relation type descriptions: Legal relation type descriptions are structures that extend re-

lation type definition frames TR. Although this kind of description was not originally listed as amodeling primitive in van Kralingen�s ontology, 10 we have chosen to introduce it so as to pre-serve a unified semantics in the model. They are used to describe relation types introduced by thelaw, such as ‘‘John has two dependent children’’.Legal act type descriptions: Legal act type descriptions are structures that extend legal relation

type descriptions. Typically, they are used to model the action associated with a norm (see Section5.4). van Kralingen lists fourteen slots for the act frame. Among all of them, ten relevant aspectsare retained as built-in graph slots––i.e. to be instantiated using conceptual structures. These are:the Agent (who did it?), the Act Type (what did he do?), the modality of Means (by what meansdid he do it?), the modality of Manner (in what manner did he do it?), the Temporal Aspect

10 In van Kralingen�s ontology of law relation types are modeled as concept types.

362 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

(when did he do it?), the Spatial Aspect (where did he do it?), the Circumstantial As-

pect (under what circumstances did he do it?), the Cause (what caused him to do it?), the Aim(what was his aim?), the Intentionality (in what state of mind did he do it?) and the FinalState (in what state did the act result?). Not every aspect proves relevant in each case. If anaspect proves irrelevant, the slot can remain uninstantiated. However, there is one exception, theslot Agent is essential to the description of an action and it must always be instantiated.

Every type described as part of legal terminological knowledge should be properly defined.Moreover, it must be a member of TC or TR. As previously noted, legal types (concept, relation oract) can hardly be defined without referring to parts of world knowledge. This globally impliesthat legal types are roughly more specialised than the ones found in the world knowledge cate-gory: legal types are generally subsumed by types from world knowledge (see, for instance, Fig. 7).

5.4. Normative knowledge representation

Normative knowledge is the central knowledge type in law. However, it cannot be describedwithout referring to both legal terminological knowledge and world knowledge. Normativeknowledge is essentially defined on the basis of individual norms. A last template is introduced inorder to represent these norms. It has been designed by deriving van Kralingen�s norm ontologicalprimitive.

Fig. 8 presents the way this extension has been achieved. The three upper boxes represent thegeneric primitives introduced in Section 4 and used to model world knowledge. The four otherones represent the templates provided as an ontological extension for the legal domain. Templatesthat derive from concept or relation type definition frames are used to define legal terminologicalknowledge, while the one that extends the graph valued frame is used to model norms.

Legal norm descriptions: Legal norm descriptions extends graph valued frames G. These repre-sentational structures are used to model individual norms. A norm is a statement to the effect thatsomething ought to, may or can be done [1]. To fulfil its function, a norm must convey a certainamount of information. The following questions must be answered by a complete norm (trans-lated p. 41 in [4] from p. 62 in [45]). Each answer corresponds to a particular aspect of a norm,which has to be modeled into a corresponding built-in slot offered by the norm template structure.

(1) Who is obliged or permitted to do something? The slot Subject is a graph slot that shouldcontain a single concept node. To make sense, the type of this node should always be sub-

Legal Act Type

Legal Relation Type

Relation Type Definition FrameConcept Type Definition Frame

Norm

Obligation Interdiction Permission

Graph valued Frame

Legal Knowledge Modeling Primitives

is ais a is a

is ais ais ais aLegal Concept Type

Fig. 8. Legal domain ontological extension.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 363

sumed by a type of which interpretation can be formulated as ‘‘an individual or an institutionwith the ability to perform actions, to conducts activities, or to sustain certain states of af-fairs’’ such as the type Person in the hierarchy in Fig. 7.

(2) Is there an obligation, an interdiction or a permission to do something or to leave somethingundone? The literal slot Legal Modality admits the following values {obligation, permis-sion, interdiction}. It determines the function of the norm by characterising it as imposingan obligation, a prohibition or granting a permission.

(3) What, where and whenmust be done or forborne? The graph slot Act is offered so as to modelthe behaviour that is the object of the norm. Its value is a conceptual graph fragment, whichobeys the following constraints:

11

spec

• It should contain a relation node rA that represents an action. Its type should have beendefined using a legal act type descriptions structure, see Section 5.3.

• The node rA should be logically linked 11 to the concept node specified in the slot Subject,which represents the subject of the norm.

In addition to these slots, the norm template structure also includes a slot Scope to express thenorm�s range of application and a graph slot Conditions of Application eventually used todescribe (when needed) circumstances under which the norm is applicable.

Actually, the previous syntactical constraints are not the only aspect by which the Legal NormDescriptions differ from their ancestors. In contrast with the graph valued frames, slots in normdescriptions carry semantics. As an example, the literal value specified in the slot Legal Mod-

ality is meaningful, e.g. ‘‘A is allowed’’ does not mean that ‘‘A is forbidden’’. Consequently, thelogical interpretation of the legal norm descriptions should be redefined to enable deontic rea-soning.

Several valid interpretations can be associated with these structures. For instance, it can beexpressed in deontic logic [1,2,46,47], which is a branch of modal logic to represent and reasonabout the normative use of language. Deontic logic revolves around the concepts of permission,prohibition and obligation. There are several arguments in favour or against the need of deonticlogic in legal knowledge representation. Some alternative formalisms had been proposed, for in-stance norms modeled as normative functions in [5] or defeasible reasoning adopted in [48,49], etc.

Deontic reasoning is a full problematics. In this paper, we confine ourselves to providing thestandardised basics for frame-based representation of the norms. Precise details about the variousways to reason about them can be found in literature, for instance [1,2,5,46–49]. However,whatever the kind of reasoning the frame structures are led to support, the projection is supposedto remain the elementary matching operation (see Section 4.4).

5.5. Example

The following sentence expresses a norm that is modeled into the frames in Figs. 9 and 10:

More precisely, between all the nodes it takes in argument, one should be co-referent with the concept node

ified as the subject of the norm.

Granting a loan for improving housing condition

Type

Id

Subject

Norm of Conduct

Act

Family Benefits Admin : *x

Scope Family Benefits

Modality Permission

Family Benefits Admin : ?x Grant Loan for Housing Impr. Claimant

Fig. 9. Example of a norm definition frame.

Grant Loan for Housing Impr.Type

Subject Family Benefits Admin : ?x

Grant

Assignment

Scope Family Benefits

Act typeFamily Benefits Admin : ?x Claimant : ?y

Loan : *z

Aim

Claimant : ?y

Loan : ?z

Home

Improvement

Live in Theme

1 2

3

Circumstances Claimant : ?y Family Benefits Admin : ?x Dispense

Arguments

Person : *x

Family Benefits Admin

Supertype Grant Loan

Person : *y

Claimant

Fig. 10. Example of an act definition frame.

364 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

Family benefits administrations are allowed to grant to their claimants loans assigned to housingconditions improvement.

The legal norm description in Fig. 9 has been constructed by answering the following questions:

(1) Is there an obligation or a permission to do something? The norm enables a category of agentsto perform a certain kind of action. This aspect is modeled into the slot Modality.

(2) What is the range of application of the norm? The norm applies to the family benefits legalsub-domain (slot Scope).

(3) Who is allowed to do something? Family benefits administrations constitute the subject of thenorm. This aspect is modeled into the slot Subject.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 365

(4) What is allowed to be done? Subjects of the norm are allowed to ‘‘grant to their claimantsloans assigned to housing conditions improvement’’. This aspect is modeled into the slotAct. However the act itself is formally defined into a separate frame (see Fig. 10).

The action type associated to this norm is modeled by the legal act type description in Fig. 10. Itextends the relation type 12

Grant Loan which takes two arguments of type Person. Then, thisact type is defined by expressing a set of constraints and conditions that marks it out from itsancestor. Each of them corresponds to a particular aspect of the legal act type, which is modeledinto a built-in slot of the associated frame structure (see legal act type description in Section 5.4).

(1) Types of the formal parameters are respectively restricted to Family Benefits Admin andClaimant. This constraint is expressed in the slot Arguments. Concept nodes drawn withdotted lines correspond to the ancestor�s formal parameters. They are related by co-referencelinks to the formal parameters of the newly defined act type. For instance, type of the secondformal parameter is restricted to Claimant. To be laid down in a valid way, this kind of con-straint must be applied between concept nodes having types such that tc;1 P tc;2. In our exam-ple, PersonPFamily Benefits Admin, see concept type hierarchy in Fig. 7.

(2) The slot Agent specifies that this legal action is performed by a family benefits administration(first argument).

(3) The slot Act Type specifies the basics of the action performed by the agent: to grant a loan toa claimant.

(4) The slot Circumstances specifies the circumstances under which this action is performed.(5) The slot Aim specifies the aim of the action: the loan is granted so that the claimant can have

his home improved.

6. Binding knowledge representation to formulating documents

As seen in Section 2.3, knowledge, knowledge formulation and knowledge representation are threeentities that should be distinguished. Knowledge formulations are collections of natural languagesigns that are used to express some knowledge. Knowledge representations are tangible artificialsubstitutes for the knowledge itself. These artifacts are usually designed with a view to makingtheir interpretation unequivocal. Modeling knowledge expressed by a set of electronic documentsentails dealing both with knowledge formulation and knowledge representation. However, sincecomputers can hardly interpret natural language signs, they cannot truly link by themselves thesigns that are used to formulate a particular piece of knowledge to the artificial ones used torepresent that part of knowledge. In this section, we briefly address the issue of encoding explicitconnections between knowledge modeled using the primitives introduced in Section 4 and thetextual document fragments that formulate it.

12 Remark: legal act type descriptions are a particular kind of relation type definitions (see Section 5.4). Thus, a legal

act type can be defined as a subtype of an already defined relation type.

366 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

6.1. Principles

Definition 5. A textual document D is a set of characters totally ordered by a relation �.

Definition 6.Given two documents D1 and D2 respectively ordered by the relations �1 and �2 , D2

is said to be a fragment of D1 if and only if:

(1) D2 is not empty, and each of its characters is also a character of D1: D2 6¼ ; and D2 � D1.(2) The total order defined by �2 on D2 corresponds to the one defined by �1: for all ðx; yÞ 2 D2 �

D2, x �1 y () x �2 y.(3) The structure hD2;�2i forms a ‘‘continuous part’’ of hD1;�1i: for all ðx; y; zÞ 2 D1 � D1 � D1,

ðx �1 y �1 z ^ fx; zg � D2Þ ) y 2 D2.

The fact that D2 is a fragment of D1 is noted D2 v D1.

For any document D, we note FðDÞ the set of all its fragments: FðDÞ ¼ fd j d v Dg. In thesame way, given a set D ¼ fD1; . . . ;Dkg of documents, we call addressable fragments of D, the setFðDÞ ¼

SDi2D FðDiÞ. Each element of FðDÞ is a document fragment, which may contain natural

language signs and may formulate knowledge.

Definition 7. Given a set D of documents and a set K of knowledge modeling units, we callrepresentational binding the relation ,

Kdefined over FðDÞ � K, which associates document

fragments to units that model the knowledge they formulate. x,K

y should be read ‘‘knowledge xformulate is modeled by y’’. We assume that any document fragment that formulates a piece ofknowledge has all its sub-fragments contributing towards this formulation: for all ðx; yÞ inFðDÞ �FðDÞ and for all z 2 K, ðx,K z ^ y v x ) y,

KzÞ.

The structures we use to represent knowledge (see Section 4) are composite ones. Framesare made of slots, which can themselves contain graph fragments. Connections between mod-eled knowledge and textual document fragments can be encoded at different levels of de-tail. Frames, slots and nodes induce three distinct granularities that can be used to makethe precision of the relationship between documents and modeled knowledge more scal-able.

• Let FS be a set of representational structures related to a support S ¼ ðTC; TR;r; I ; sÞ made up ofall the type definition frames that are used to define non primitive types in TC and TR in con-junction with an unrestricted number of graph valued frames defined on S: FS ¼ fTC;1; . . . ;

TC;kg [ fTR;1; . . . ;TR;lg [ fG1; . . . ;Gmg. We note ,FS

the representational binding defined over

FðDÞ � FS, which associates document fragments with the frames. For instance, d ,KTC;i

should be read as ‘‘the type definition TC;i is (partly) available from the fragment d.’’• Let SðFSÞ ¼

SFSSi be the set of all frame slots that are contained by modeling structures in

FS. The representational binding defined over FðDÞ �SðFSÞ is noted ,S

. It associates docu-ment fragments with the various slots of the frames.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 367

• The set of all concept and relation nodes that appear in structures in FS is notedNðFSÞ ¼

SFSðRi [ CiÞ. We note ,

Nthe representational binding defined over FðDÞ�

NðFSÞ, which associates document fragments such as words to nodes that appear in thegraph slots.

The main purpose of the representational bindings is to keep track of where the informationused for modeling a certain element of knowledge came from. ,

FS, ,

Sand ,

Nlink document

sources to modeled knowledge at three levels of granularity respectively corresponding to frames,slots and nodes.

Knowledge is modeled into hierarchical structures: nodes are nested in slots, which arethemselves nested in frames. We assume that these hierarchical structures match with similarones in documents. Let hasSlot be a relation between slot elements and frames such thathasSlotðf ; sÞ is true if the slot s belongs to the frame f , and hasNode be a relation betweennodes (concepts or relations) and slots such that hasNodeðf ; nÞ is true if the node n appears inthe slot s through the v application (see Section 4.2). Connections between knowledge repre-sentation and formulating document sources should be modeled so as to preserve both thefollowing constraints:

(1) Every document fragment linked to a frame slot should also be linked to the frame this slotbelongs to: for all f 2 FS, for all s 2 SðFSÞ and for all d 2 FðDÞ, ðhasSlotðf ; sÞ ^d ,

SsÞ ) d ,

FS f .

(2) Every document fragment linked to a graph node (concept or relation) should also be linkedto the slot this node belongs to: for all s 2 SðFSÞ, for all n 2 NðFSÞ and for all d 2 FðDÞ,ðhasNodeðs; nÞ ^ d ,

NnÞ ) d ,

Ss.

Remark 8. Document structuring languages such as SGML [50] or XML [51] implement a mark-up system, which enables explicit information to be bound to the text itself. Nowadays, XML hasbecome one of the most commonly used structuring formats. In such a language, documents arehandled as ordered hierarchies of content objects. In case the documents to link the modeledknowledge with are formated using XML or SGML, the notion of document fragment as in-troduced in Definition 6 should not be confused with the hierarchies denoted by the nesting re-lation between document elements.

6.2. Example

Fig. 11 presents two textual documents that introduce parts of the definition for the legalconcept type ‘‘Claimant’’. Such a definition is referred as ontological knowledge and is modeledinto a Legal Concept Type Description Structure (see Section 5.4) and frame that models aconcept type definition.

Both documents are structured according to a logical point of view – i.e. their structures reflecttheir organisation in books, chapters, sections, articles, etc. The definition they express is modeledinto the frame structure. In the first document, the definition is given in the article art. L. 512-1. Inthe second document, it is defined in the section I-Claimant. Knowledge the frame structure

Book 5: Family benefits and related benefits

Title I: Application Scope - Generalities...

Chapter II: Application Scope

art. L. 512-1 Each french person or foreigner residing in France, having one or more children in its charge ...

Social Security Code...

Legislative Follow-Up : CEE

I - Claimant

I.1 General Conditions I.1.a Person Related Conditions

I.1.c Residence Related Conditions

...

I.1.d Children Related Conditions...

Claimant

Supertypes

ResidenceCondition

ActivityCondition

Physical Person : *x

Type

Activity

Physical Person : ?x France Reside in

Practise France LocationPhysical Person : ?x

Child :{*}

European Union

Reside inPhysical Person : ?x RelativeChidrenrelated

Condition

I.1.b Activity Conditions...

Scope Family Benefits

......

... resides in ...

FS SNN

Fig. 11. Binding knowledge representation to knowledge formulation.

368 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

represents can be (partly) read from the fragments encircled by these content elements. Conse-

quently, those fragments and this frame structure are linked 13 together through the relation ,FS

.

At a finer granularity level, this definition has three different aspects. Each of them is modeledas a particular slot in the frame. The section I-Claimant in the second document has severalsubsections. Each of them expresses a particular aspect of the definition and is related to thecorresponding slot in the frame through the relation ,

S.

At the finest granularity level, nodes appearing in the graph slots are linked to word collectionsin those sections or subsections through the relation ,

S. As an example, the relation node

(Reside in) is linked to ‘‘residing in’’ in the article art. L. 512-1 of the first document. Themeaning of such a link is straightforward: (Reside in) and ‘‘residing in’’ express both the samepiece of knowledge.

Remark 9. For didactic purposes, the fragments to be linked here with modeling units (frames,slots or nodes) correspond to various elements of a document�s logical structure (chapter, section,subsection, article, paragraph, etc.). It is essential to notice that this is just a particular case. The

13 Please note that Fig. 11 does not show all the links between the frame and the documents.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 369

way a document is structured into sections, subsections, paragraphs, etc. does not necessarilymatch with the way the formulated knowledge is modeled. For instance, the knowledge modeledby a slot may well be read from a fragment that overlaps several paragraphs.

7. Framework implementation

The features introduced in this paper have been implemented into a prototypic platform writtenin Java. It has been designed to provide the minimal set of functionalities for document andknowledge management in the legal domain. At present, the platform is made up of the followingcomponents:

Knowledge server: It hosts a knowledge base made of a support (type hierarchies, catalogue ofindividuals, etc.) in conjunction with an unrestricted number of frame structures that are used toexpress type definitions and/or factual knowledge. It offers basics and general-purpose services viaan object-oriented distributed API: adding, removing, modifying, retrieving, combining knowl-edge structures, type expansion, graph matching, etc. The ontological extension for the legaldomain has been implemented as an additional module of the server. This server is built upon theconceptual graph platform Notio [52].Document server: This component is dedicated to document management and principally serves

functionalities such as document storage, document retrieval, fragment extraction, etc.Reference manager: This component acts as a mediator between knowledge and document

servers. Its role is to manage the relationship between knowledge elements and document frag-ments. For instance, when the knowledge server receives a request ‘‘return the fragments attachedto this slot’’, the reference manager�s link resolution facilities are activated, the fragments to beextracted are identified and the document server�s extraction services are invoked with the rightparameters. 14

Knowledge editor: It constitutes a semantic authoring tool that enables firstly to acquireknowledge, and secondly to relate modeled knowledge to document fragments. Fig. 12 presents ascreenshot of the editor.Web portal: A set of servlets is used to generate dynamic pages. Final users browse legal

documents at the conceptual level. Modeled knowledge offers an efficient way to access the legalinformation expressed in the documents. At the same time, modeled knowledge can be accessedfrom textual information. Maintaining the knowledge base is made easier when alterations aremade in document sources.

Section 4 mainly specifies an abstract syntax for the frame structures. On this basis, concretesyntax may be designed according to particular purposes such as compact storage, humanreadability, efficient parsing, application interoperability, etc. For instance, the graphical repre-sentation given in Section 4.5 is a concrete notation proposed in order to ease human/machineinteractions. Actually, in the earlier versions of the knowledge server, the frame structures arestored using XML. Our choice is debatable. Effectively, a textual notation called conceptual graph

14 A similar process is performed to reply the opposite request ‘‘which are the knowledge modeling elements bound to

a particular document fragment?’’

Fig. 12. Graphical editor for knowledge acquisition.

370 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

interchange form (CGIF) [53] has been specially designed to enable the efficient communication ofconceptual graphs between machines. Perhaps, a more suitable solution would be to extend CGIFby integrating the notions of frame and slot. The resulting notation would very likely allow moreefficient storage and parsing.

In Section 6 we did not give any specifications relating to the format of the documents to belinked with knowledge representations. In our implementation, we have chosen to store docu-ments using XML. The fragments are marked up using anchor elements kept within documents.

Straightforwardly, the relations ,FS

, ,S

and ,N

that link document sources to modeled knowledgeare implemented as mapping tables between anchor and knowledge elements.

Presently, we do not consider this project as completed. Basic components have been imple-mented. Document fragments, modeled knowledge and binding features form an hypertextualsystem that gives some interesting results. However, we plan to study more specialised tasks andto plug tools such as normative consistency analyzer into the platform.

8. Discussion

8.1. Graphical knowledge representation

Computer applications that use knowledge representations are not only accessible to computerscientists. Domain experts have to interact with the program so as to capture domain knowledge,to carry out checks, to consult some results, or simply to visualise knowledge content.

Representational structures introduced in this paper are based on conceptual graphs. Theybenefit from their visual aspect. Experts appreciate graphic notations, whereas they seem to be

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 371

reluctant to deal with textual formal languages. By making human/machine interactions easier,the graphical representation is significant for knowledge acquisition and representation.

Besides being easily interpretable, these structures inherit conceptual graphs� expressivenessand natural language matching adequacy. They can form the basis for a specialised commu-nication language between specialists of different disciplines involved in a common cognitivework.

8.2. Frame based representational structures

Actually, conceptual graphs make modeled knowledge understandable by experts with littleeffort. However, when expressing a massive amount of knowledge, graphs increase in size and itbecomes more and more difficult for humans to read and understand them. Logically dividing asingle complex graph into several consistent annotated units can make it easier to understand.

Representational structures presented in this paper are inspired by frame structures. Frames aredefined as data-structures for representing stereotyped situations. Conceptual graphs are laid outinto frame structures. Slots introduce an intermediate granularity level between the graph and thenodes. They are used to model major aspects of the stereotyped situation. They provide an ad-ditional information structure which can be computationally processed and be helpfully used inhuman/machine interactions. Experts, guided by structural annotative information, can succes-sively focus on the different parts of the graph, and finally have an easier global comprehension ofthe modeled knowledge. Thus, integrating some frame structural aspects to conceptual graphallows to construct powerful and user-friendly human/machine interfaces such as knowledgeeditors. Moreover, structural information provided by slots can be processed to perform efficientgraph fragment indexing.

8.3. Ontological extensions

The modeling framework proposed in this paper contains constructs that are thought to begeneric for several domains. That is, graph valued frames, concept type definition frames andrelation type definition frames are considered to be relevant primitives in many domains.

However this framework has been designed so as to be sufficiently modular to accept manyfurther domain specific extensions. Such an extension is provided for legal domains. It introducesspecific constructs to properly handle knowledge found in legal domains. The legal extensionderives from van Kralingen�s ontology of law. It contains constructs that are thought to be genericfor the legal domain. Norms, acts and concept descriptions are considered to be present in anylegal domain [4]. However, modeling a legal sub domain also involves deciding upon numerousontological questions. For instance, distinctions made between types in family benefits sub-domain may be useless in the unemployment benefits one. Types and type hierarchies shouldalways be created for each legal sub domain under consideration.

8.4. Document formal explanation

Binding features are introduced so as to link knowledge representations to textual sourcesthat formulate the modeled knowledge. They are both a means of keeping track of where the

372 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

information used for modeling a certain element of the knowledge base came from and a means ofmarking document fragments by non-equivocal representations of the knowledge they express.For instance, they encode the fact that the definition of the concept type ‘‘Claimant’’ is based onthe article art. L. 512-1 of the Social Security Code and the section I-Claimant of the LegislativeFollow-Up: CEE.

From a functional viewpoint, they provide basics for conceptual information retrieval andpermit the knowledge base to be a formal description of documentation content. From a morepractical viewpoint, this connection is fundamental for maintaining the knowledge base whenalterations are made in the textual information.

Connections between modeled knowledge and textual document fragments can be encoded atdifferent levels of detail. Document fragments are bound to knowledge representation with respectof three granularities. Frame, slot and node bindings make the precision of the relationship morescalable. Applications can work at the level of details that best suits their purposes. For instance,the finer granularity level that relates nodes to word collections may provide too much precisionand may be irrelevant for some applications.

8.5. Applications

The framework introduced in this paper may be used as basis for building many kinds ofsystems dealing both with legal knowledge and legal documents, for instance:Coherence management: Representational structures may be processed using projection algo-

rithms in order to detect inconsistencies, contradictions or tautologies in both the regulation andlegal documentation.Impact study on legal documentation: Simulation tools may be built upon the model so as to

simulate and estimate the impact of regulation changes on legal documentation.Conceptual retrieval: Concept-based legal information retrieval systems may be designed upon

the basis of the framework we suggest. The modeled knowledge and the connections to the legaldocuments can easily be processed so as to return legal information that is conceptually related tousers� questions. Such systems should be an efficient way to handle legal rule fragmentationphenomenon mentioned in Section 2.2.Semantic Web portal: Regulation content may be presented and made available to users

through web portals. The structure and organisation of those portals may be automatically de-termined from modeled knowledge thus providing an efficient access to information.

9. Conclusion

The main contribution of this paper is to lay down a conceptual framework for documentsemantics modeling. Its key aspects are user-friendliness, re-usability, extensibility and com-putability. From this basis, an ontological extension has been provided so as to properlyhandle the particular kind of knowledge encountered in the legal domain. This extension de-rives from van Kralingen�s ontology of law and provides basics for many knowledge-basedmanagement tools for legal documents: coherence management, impact study, conceptual re-trieval, etc.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 373

References

[1] G.H. von Wright, Norm and Action: A Logical Enquiry, International Library of Philosophy and Scientific

Method, Routledge and Kegan Paul, London, 1963.

[2] L. Royakkers, Combining deontic and action logics for collective agency, in: Legal Knowledge and Information

Systems, JURIX 2000: The Thriteenth Annual Conference, IOS Press, Amsterdam, 2000, pp. 135–146.

[3] L. McCarty, A language for legal discours I. Basic structures, in: Proceedings of the Second International

Conference on Artificial Intelligence and Law, ACM, Vancouver, Canada, 1989, pp. 180–189.

[4] R.W. van Kralingen, Frame-Based Conceptual Models of Statute Law, Computer/Law series 16, Kluwer Law

International, The Hague, 1995.

[5] A. Valente, Legal knowledge engineering, a modelling approach, Ph.D. thesis, University of Amsterdam,

Amsterdam, The Netherlands, 1995.

[6] T.J.M. Bench-Capon, Arguing with cases, in: Legal Knowledge Based Systems JURIX 97: The Tenth Conference,

The Vrije Universiteit, 1997, pp. 85–99.

[7] E. Pietrosanti, B. Graziadio, Advanced techniques for legal documents processing and retrieval, Artificial

Intelligence and Law 7 (4) (1999) 341–361.

[8] R. Winkels, D. Bosscher, A. Boer, R. Hoekstra, Extended conceptual retrieval, in: Legal Knowledge and

Information Systems, JURIX 2000: The Thriteenth Annual Conference, IOS Press, Amsterdam, 2000, pp. 85–97.

[9] M.-F. Moens, Innovative techniques for legal text retrieval, Artificial Intelligence and Law 9 (2001) 29–57.

[10] D. Merkel, E. Schweighofer, W. Winiwarter, Exploratory analysis of concept and document spaces with

connectionist networks, Artificial Intelligence and Law 7 (2–3) (1999) 185–209.

[11] D. Jouve, B. Chabbat, Y. Amghar, J.-M. Pinon, L�archivage des documents r�eeglementaires: maııtrise d�une mati�eerepremi�eere sp�eecifique, Document Num�eerique 4 (3–4) (2001) 315–329.

[12] H. Hart, The Concept of Law, Clarendon Law Series, Oxford University Press, Oxford, 1961.

[13] A. Ross, Directives and Norms, Clarendon Law Series, Humanities Press, New York, 1968.

[14] H. Kelsen, Pure Theory of Law, University of California Press, Berkley, USA, 1978, Translation of ‘‘Reine

Rechtslehre’’ (second revised edition, 1960) by Max Knight, First published in 1934.

[15] G. von Wright, Practical Reason, Philosophical Papers, Blackwell, Oxford, 1983.

[16] H. Kelsen, General Theory of Norms, Clarendon Press, Oxford, UK, 1991, Translation of ‘‘Allgemeine Theorie der

Normen’’ by Hartney.

[17] R. van Kralingen, E. Reurings, E. Oskamp, Norm frames in the representation of laws, in: Legal Knowledge Based

Systems JURIX 93: Intelligent Tools for Drafting Legislations, Computer-Supported Comparison of Law,

Knoinklijke Vermande, Lelystad, 1993, pp. 11–22.

[18] D. Jouve, B. Chabbat, Y. Amghar, J.-M. Pinon, Structuration s�eemantique de la r�eeglementation, in: Proceedings of

the XIX�eeme Congr�ees INFormatique des ORganisations et Syst�eemes d�Information et de D�eecision (INFOR-

SID�2001), INFORDSID Editions, Martigny, Suisse, 2001, pp. 5–26.

[19] R. Davis, H. Shrobe, P. Szolovits, What is a knowledge representation?, AI Magazine 14 (1) (1993) 17–33.

[20] A. Rand, Introduction to Objectivist Epistemology, New American Library, New York, USA, 1979.

[21] R.K. Stamper, The role of semantics in legal expert systems and legal reasoning, Ratio Juris 4.

[22] R.K. Stamper, Signs, information, norms and systems, in: B. Holmqvist, P.B. Andersen (Eds.), Signs of Work:

Semiotics and Information Processing in Organisations, De Bruyter, Berlin, Germany, 1996, pp. 349–397.

[23] T.R. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisition 5.

[24] P.R.S. Visser, T.J.M. Bench-Capon, A comparison of four ontologies for the design of legal systems, Artificial

Intelligence and Law 6 (1) (1998) 27–57.

[25] P.R.S. Visser, Knowledge Specification for Multiple Legal Tasks; A Case Study of the Interaction Problem in the

Legal Domain, Computer/Law serie 17, Kluwer Law International, The Hague, The Netherlands, 1995.

[26] R.W. van Kralingen, A Conceptual Frame-based Ontologie for the Law, in: R.G.F. Winkels, P.R.S. Visser, (Eds.),

Proceedings of the First International Workshop on Legal Ontologies (LEGONT�97), Melbourne, Australia, 1997,

pp. 15–22.

[27] R.W.V. Kralingen, P.R.S. Visser, T.J.M. Bench-Capon, H.J.V.D. Herik, A principled approach to developing legal

knowledge systems, International Journal of Human–Computer Studies 51 (1999) 1127–1154.

374 D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375

[28] B. Chabbat, Mod�eelisation multiparadigme de textes r�eeglementaires, Ph.D. thesis, Institut National des Sciences

Appliqu�eees de Lyon, Lyon, France, D�eecembre 1997.

[29] D. Jouve, B. Chabbat, Y. Amghar, J.-M. Pinon, Mod�eelisation s�eemantique de la r�eeglementation, Ing�eenierie des

Syst�eemes d�Information 6 (2) (2001) 95–119.

[30] M. Sergot, The representation of law in computer programs: a survey and comparison of past and current projects,

in: T. Bench-Capon (Ed.), Knowledge Based Systems and Legal Applications, Academic Press, 1990, pp. 3–67.

[31] A. Valente, J. Breuker, B. Brouwer, Legal modeling and automated reasoning with on-line, International Journal

of Human–Computer Studies 51 (1999) 1079–1125.

[32] J. Breuker, Toward a workbench for legal practitioner, in: C. van Noortwijk, A. Schmidt, R.G.F. Winkels (Eds.),

Legal Knowledge Based Systems: Aims for Research and Development, Knoinklijke Vermande, 1990, pp. 25–36.

[33] J. Breuker, N. den Haan, Separating world and regulation knowledge: Where is the logic? in: M. Sergot (Ed.),

Proceedings of the Third International Conference on AI and Law, 1991, pp. 41–51.

[34] J.F. Sowa, Conceptual Structures Information Processing in Mind and Machine, The System Programming Series,

Addison-Wesley, 1984.

[35] D.D. Roberts, The Existential Graphs of Charles S. Peirce, Mouton, The Hague, 1973.

[36] J.F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole

Publishing Co., Pacific Grove, USA, 2000.

[37] M.-L. Mugnier, M. Chein, Repr�eesenter des connaissances et raisonner avec des graphes, Revue d�intelligence

artificielle 10 (1) (1996) 7–56.

[38] M. Chein, M.-L. Mugnier, Conceptual graphs: fundamental notions, Revue d�intelligence artificielle 6 (4) (1992)

365–406.

[39] M. Lecl�eere, C-CHic: Construction coop�eerative de hi�eerarchies de cat�eegories, Revue d�intelligence artificielle 10 (1)

(1996) 57–100.

[40] M.L. Mugnier, Knowledge representation and reasonings based on graph homomorphism, in: B. Ganter, G.

Mineau (Eds.), Proceedings of the 8th International Conference on Conceptual Structures (ICCS�00), vol. 1867 of

Lecture Notes in AI, Springer, Darmstadt, Germany, 2000, pp. 172–192.

[41] C. Bos, B. Botella, P. Vanheeghe, Modeling and simulating human behaviors with conceptual graphs, in:

Proceedings of the 5th International Conference on Conceptual Structures (ICCS�97), vol. 1257, Springer Verlag,

1997, pp. 275–289.

[42] W.A. Wulf, M. Shaw, P.N. Hilfinger, L. Flon, Fundamental Structures of Computer Science, Addison-Welsey,

1981.

[43] M.-L. Mugnier, On generalization/specialization for conceptual graphs, Journal of Experimental and Theoretical

Artificial Intelligence 7 (1995) 325–344.

[44] M. Chein, M.L. Mugnier, G. Simonet, Nested graphs: a graph-based knowledge representation model with FOL

semantics, in: Proceedings of the 6th International Conference Principles of Knowledge Representation and

Reasoning (KR�98), Morgan Kaufmann Publishers, Trento, Italie, 1998, pp. 524–534.

[45] P.W. Brouwer, Samenhang in recht: een analytishe studie, Rechtswetenschappelijke Reeks, Wolters-Noordhoff,

Groningen , The Netherlands, 1990.

[46] P. Bailhache, Authorities and addressees in deontic logic: indexed operators and action, in: J.-J.C. Meyer, R.J.

Wieringa (Eds.), Proceedings of the First International Worshop on Deontic Logic in Computer Science,

Amsterdam, 1991, pp. 72–88.

[47] L. Royakkers, F. Dignum, The idea of obligation or: How to interpret O(p)? in: Legal Knowledge Based Systems,

JURIX 95: Telecommunication and AI and Law, Knoinklijke Vermande, Lelystad, 1995, pp. 105–112.

[48] Y.U. Ryu, Conditional deontic logic augmented with defeasible reasoning, Data and Knowledge Engineering 16

(1995) 73–91.

[49] Y.U. Ryu, R.M. Lee, Defeasible deontic reasoning and its applications to normative systems, Decision Support

Systems 14 (1995) 59–73.

[50] International Organization for Standardization, Standard Generalized Markup Language, International Standard

ISO 8879:1986(E), 1986.

[51] World Wide Web Consortium, Extensible Markup Language (XML) 1.0 (Second Edition), W3C Recommenda-

tion. Available from <http://www.w3.org/tr/rec-xml> 2000.

D. Jouve et al. / Data & Knowledge Engineering 46 (2003) 345–375 375

[52] F. Southey, J.G. Linders, Notio––a Java API for developing CG tools, in: Proceedings of the 7th International

Conference on Conceptual Structures (ICCS�99), in: W. Tepfenhart, in: W. Cyre (Eds.), Lecture Notes in AI,

Springer, Berlin, 1999, pp. 262–271.

[53] J.F. Sowa, Conceptual graphs: draft proposed American National Standard, in: W. Tepfenhart, W. Cyre (Eds.),

Conceptual Structures: Current Research and Practice, Lecture Notes in AI # 1640, Springer-Verlag, Berlin, 1999,

pp. 1–65, an updated version is available at http://www.jfsowa.com/cg/cgstand.htm.

David Jouve is a Ph.D. student at the National Institute of Applied Sciences (INSA) of Lyon, France. At thesame time, he is in charge of the Regulation Semantics Modelling project at the Information System Divisionof the Caisse Nationale des Allocations Familiales, a branch of the French National Health Care System. Hisresearch interests include knowledge representation, conceptual modelling and document engineering, withdirect applications in knowledge and document management in the legal domain.

Youssef Amghar is Assistant Professor of management information systems at the Scientific and TechnicalUniversity of Lyon. He holds a Ph.D. in Computer Science from the same University in 1989. His field ofteaching includes project management, databases and development processes. He is an active member oflaboratory of information system of INSA de Lyon. His current research interests include semantics andconsistency of data, interoperability between applications and legal documents. He is the author of severalpapers related to these research activities and managed some projects about decisions support. Currently, he isresponsible for a research team working on the domain of enterprise memory and knowledge management.

Bertrand Chabbat has received his Ph.D. degrees in Computer Science from the National Institute of AppliedSciences (INSA) of Lyon, France. His thesis concerned multi-paradigmatic techniques for legal documentmanagement. Currently, he is in charge of the Documentary Architecture and Legal Database projects atInformation System Division of the Caisse Nationale des Allocations Familiales (CNAF).

Jean-Marie Pinon is Professor at the National Institute of Applied Sciences (INSA) of Lyon, France. Cur-rently, he is the Chairman of the Computer Science Department. He is responsible for a research team ondocuments and decision-support systems at the information system laboratory. His personal research interestscover the areas of hypermedia and document information systems, decision-support applications, human/machine interfaces. He has published over 100 journal and conference papers in these areas and supervisedmore than 15 Ph.D. programs.