=Paper=
{{Paper
|id=Vol-321/paper-3
|storemode=property
|title=The LKIF Core Ontology of Basic Legal Concepts
|pdfUrl=https://ceur-ws.org/Vol-321/paper3.pdf
|volume=Vol-321
|dblpUrl=https://dblp.org/rec/conf/icail/HoekstraBBB07
}}
==The LKIF Core Ontology of Basic Legal Concepts==
https://ceur-ws.org/Vol-321/paper3.pdf
The LKIF Core Ontology of Basic Legal Concepts
Rinke Hoekstra, Joost Breuker, Marcello Di Bello, Alexander Boer
Leibniz Center for Law, University of Amsterdam
breuker@science.uva.nl, hoekstra@uva.nl, mbello@science.uva.nl, aboer@uva.nl
Abstract. In this paper we describe a legal core ontology that is part of a generic
architecture for legal knowledge systems, which will enable the interchange of know-
ledge between existing legal knowledge systems. This Legal Knowledge Interchange
Format, is under development in the Estrella project and has two main roles: 1)
the translation of legal knowledge bases written in different representation formats
and formalisms and 2) a knowledge representation formalism that is part of a larger
architecture for developing legal knowledge systems. A legal (core) ontology can
play an important role in the translation of existing legal knowledge bases to other
representation formats, in particular into LKIF as the basis for articulate knowledge
serving. We describe the methodology underlying the LKIF core ontology, introduce
the concepts it defines, and discuss its use in the formalisation of an EU directive.
Keywords: ontology, legal ontology, legal concept, LKIF, knowledge representation,
framework
1. Introduction
In this paper we describe a legal core ontology that is part of a ge-
neric architecture for legal knowledge systems, which will enable the
interchange of knowledge between existing legal knowledge systems.
This Legal Knowledge Interchange Format (LKIF), is currently being
developed in the Estrella project.1 LKIF has two main roles: enable the
translation between legal knowledge bases written in different represen-
tation formats and formalisms and secondly, as a knowledge representa-
tion formalism that is part of a larger architecture for developing legal
knowledge systems. These use-cases for LKIF bring us to the classical
trade-off between tractability and expressiveness, as in e.g. KIF (Know-
ledge Interchange Format, (Genesereth and Fikes, 1992)). An additional
requirement is that LKIF should comply with current Semantic Web
standards to enable legal information serving via the web: the core
of LKIF consists of a combination of OWL-DL and SWRL, offering a
classical hybrid solution. How these two formalisms have to be combined
still is an important issue in the development of LKIF, and for details
the reader is referred to (Boer et al., 2007).
1
Estrella is a 6th European Framework project (IST-2004-027665). See also:
http://www.estrellaproject.org. The views and work reported here are those of
the authors.
44 Hoekstra, Breuker, Di Bello, Boer
Proposing the OWL-DL subset of SWRL as its core does not make
LKIF a formalism tuned to legal knowledge and reasoning: how do we
get the ‘L’ into LKIF? To “legalize” LKIF it needs to be constrained
in two ways. The first is a meta-component that controls the reason-
ing as to gear it to typical legal tasks. For instance, legal assessment
and argumentation provide control structures for legal reasoning that
put specific demands on the knowledge to be obtained from a legal
knowledge base. The second constraint is not specialised to legal rea-
soning, but to legal knowledge. Typical legal concepts may be strongly
interrelated and thereby provide the basis for computing equivalen-
cies (paraphrases) and implications. For instance, by representing an
obligation as the opposite of a prohibition, a (legal) knowledge system
can make inferences that are specialised to these terms. In our view,
specialised legal inference should be based on definitions of concepts
involved in an ontology. Concept definitions should make all necessary
and sufficient interrelationships explicit; the inference engine can then
generate all implied consequences. 2
A legal ontology can play an important role in the translation of
existing legal knowledge bases to other representation formats, in par-
ticular into LKIF as the basis for articulate knowledge serving. Similar
to a translation between different natural languages, a formal, ‘syntac-
tic’ translation may clash with the semantics implied by the original
knowledge representation. An ontology, as representation of the se-
mantics of terms, allows us to keep track of the use of terms in a
knowledge base. Furthermore, and more importantly, an ontology can
support the process of knowledge acquisition and modelling in legal
domains. Defining concepts like ‘norm’, ‘judge’, ‘liability’, ‘document’,
‘claim’, etc. helps to structure the process of knowledge acquisition.
Earlier experience, as in e.g. (Breuker and Hoekstra, 2004b; Breuker
and Hoekstra, 2004a), suggests a commonsense basis for distinguishing
main categories in an ontology for law.
The following sections describe the theoretical and methodological
framework against which the LKIF core ontology has been developed
(Section 2 and 3). Section 4 describes the different modules of the
ontology, and introduces its most important concepts. Section 5 gives
an example of how the ontology can be used in the formalisation of a
regulation.
2
For an ontology cast in OWL-DL these inference engines are description
classifiers, e.g. Pellet, http://pellet.owldl.com/
The LKIF Core Ontology of Basic Legal Concepts 45
2. Frameworks and Ontologies
We adhere to a rather restrictive view on what an ontology should
contain: terminological knowledge, i.e. intensional definitions of con-
cepts, represented as classes with which we interpret the world. The
distinction between terminological knowledge (T-Box) and assertional
knowledge (A-Box) has already been around for a long time. As a rule,
terminological knowledge is generic knowledge while assertional know-
ledge describes the (actual) state of some world: situations and events.
However, these asserted states can become generalised into typical pat-
terns related to particular situations. To be sure, if experiences re-occur
and have a justifiable structure, it might evidently pay to store these
structures as generic descriptions, because they deliver a predictable
course of events for free. Eating in a restaurant is a typical example
and it served in the Seventies to illustrate the notion of knowledge
represented by scripts (Schank and Abelson, 1977) or ‘frames’ (Minsky,
1975). This kind of generic knowledge is indeed rooted in terminological
knowledge, but is structured differently. Where ontologies have a taxo-
nomic structure, frames are dominated by mereological and dependency
relationships.
Finally, an important reason to distinguish frameworks from on-
tology proper is that frameworks often imply epistemic roles which
require reasoning architectures that go beyond the services provided
by OWL-DL reasoners (e.g. meta-level reasoning). It should be noted
that frameworks are generic, i.e. they act as pre-specified patterns that
get instantiated for particular situations. We have distinguished the
following types of frameworks:
Situational frameworks Situational frameworks are stereotypical struc-
tures of plans for achieving some goal in a recurrent context. Making
coffee may be such a plan. However, the plans may involve transactions
in which more than one actor participates. For instance, the definition
of Eating-in-a-restaurant3 shows the dependencies between actions of
clients (ordering, paying) and service personnel (noting, serving) as its
major structure. This is the internal structure of the concept, but it
usually does not make sense to create class-subclass relations between
such frame-like concepts. The Eating-in-a-restaurant is not some natural
sub-class of Eating. It refers to some typical model of how eating is
put in the context of a restaurant. We can introduce a proliferation of
all contexts of eating, such as Eating-at-home, Eating-with-family, etc.
but these contexts do not fundamentally differ, cf. (Bodenreider et al.,
3
In the following all concepts will start with a capital, properties and relations
will not
46 Hoekstra, Breuker, Di Bello, Boer
2004; Breuker and Hoekstra, 2004a). In the legal world, such situational
frameworks may be pre-scribed in articles of procedural (‘formal’) law.
Although stereotypical plans (‘customs’) and prescribed plans may differ
in their justification – rationality vs. authority – their representation is
largely analogous. Similarly, legal norms combine generic situation de-
scriptions with some specific state or action. The description is qualified
by a deontic term. For instance, the norm that “vehicles should keep to
the right of the road” states that the situation in which a vehicle keeps
to the right is obliged.
Mereological frameworks Many entities, both objects and processes
often have parts: they are composites. It is tempting to include a mere-
ological (part-of) view in the definition of a concept. For instance,
defining a car as having at least three, and usually four wheels, and
at least one motor. However, a full structural description of all its parts
and connections goes beyond what a car essentially is. Mereological
frameworks appear under a large diversity of names: structural models,
configurations, designs, etc. Arguably, the distinction between a mere-
ological framework and a defining description of a term (ontology) is
sometimes be very thin. For instance, if we want to describe a bicycle
as distinct from a tricycle, it is necessary to use the cardinality of the
wheels as defining properties as these are central to the nature of the
bicycle. On the other hand, the number of branches a tree might have
hardly provides any information as to what a tree is.
Epistemological frameworks Inference structures are often represented
as epistemological frameworks of interdependencies between reasoning
steps. Typical examples are the problem solving methods (PSM) found
in libraries of problem solving components (Breuker and Van de Velde,
1994; Motta, 1999; Schreiber et al., 2000)4 A problem solving method is
not only a break-down of a problem, but also provides control over the
making of inferences by assessing success and failure in arriving at the
(sub)goals. PSMs have two major components: some method for select-
ing or generating potential solutions (hypotheses), and some methods
for testing whether the solutions hold. Whether they hold may be due
to the fact that they satisfy all the specified requirements (constraints)
or whether they correspond with (‘explain’) empirical data.
This focus on the use of knowledge, its epistemological status (e.g.
hypothesis vs. conclusion) and the dependencies between distinct steps
in a methodology is characteristic for epistemological frameworks. Epis-
4
Although the terms ‘reasoning’ and ‘inference’ are often used as more or less
synonymous, we want to reserve the term inference for making explicit what is
implicit in a knowledge base, given some inference engine.
The LKIF Core Ontology of Basic Legal Concepts 47
temological frameworks can be more abstract than PSMs. For instance,
the Functional Ontology of Law, which is presented as a core ontology, is
an epistemological framework that describes the role of law as a control
system in society (Valente, 1995; Breuker et al., 2004).
3. Methodology
The construction of LKIF followed a combination of methodologies for
ontology engineering. Already in the mid-nineties, the need for a well-
founded methodology was recognised, most notably by (Gruber, 1994;
Grüninger and Fox, 1995; Uschold and King, 1995; Uschold and Grü-
nin-ger, 1996) and later (Fernández et al., 1997). These methodologies
follow in the footsteps of earlier experiences in knowledge acquisition,
such as the CommonKADS approach (Schreiber et al., 2000) and others,
but also considerations from naive physics and cognitive science, such
as (Hayes, 1985) and (Lakoff, 1987), respectively.
(Hayes, 1985) describes an approach to the development of a large-
scale knowledge base of naive physics. Instead of rather metaphysical
top-down construction, his approach starts with the identification of
relatively independent clusters of closely related concepts. These clus-
ters can be integrated at a later stage, or used in varying combinations
allowing for greater flexibility than monolithic ontologies. Furthermore,
by constraining (initial) development to clusters, the various – often
competing – requirements for the ontology are easier to manage.
Whereas the domain of (Hayes, 1985)’s proposal concerns the rela-
tively well-structured domain of physics, the combination of common-
sense and law does not readily provide an obvious starting point for the
identification of clusters. In other words, for LKIFcore, we cannot carve-
up clusters from a pre-established middle ground of commonsense and
legal terms. Furthermore, the field does not provide a relatively stable
top level from which top-down development could originate.
In (Uschold and King, 1995), who are the first to use the term
‘middle-out’ in the context of ontology development, it is stressed that
the most ‘basic’ terms in each cluster should be defined before moving
on to more abstract and more specific terms within a cluster. The
notion of this basic level is taken from (Lakoff, 1987), who describes
a theory of categorisation in human cognition. Most relevant within the
context of ontology engineering (Uschold and King, 1995; Lakoff, 1987,
p. 12 and 13) are basic-level categorisation, basic-level primacy and
functional embodiment. Categories are organised so that the categories
that are cognitively basic are ‘in the middle’ of a taxonomy, gener-
alisation proceeds ‘upwards’ from this basic level and specialisation
48 Hoekstra, Breuker, Di Bello, Boer
proceeds ‘downwards’. Furthermore, these categories are functionally
and epistemologically primary with respect to (amongst others) know-
ledge organisation, ease of cognitive processing and ease of linguistic
expression. Basic level concepts are used automatically, unconsciously,
and without noticeable effort as part of normal functioning. They have
a different, and more important psychological status than those that
are only thought about consciously.
For the purpose of the LKIF ontology, we have made slight adjust-
ments to the methodology of (Hayes, 1985; Uschold and Grü-nin-ger,
1996). We established design criteria for the development of the LKIF on-
tology based on (Gruber, 1993; Uschold and Grü-nin-ger, 1996). These
criteria were implemented throughout the following phases: identify
purpose and scope, ontology capture and coding, integration with exist-
ing ontologies and evaluation. The following section describes how these
phases have materialised in the context of LKIF Core. Furthermore, an
example in which the ontology is put to use is described in section 5.
.
4. Modules & Outline
This section describes how the methodology described in the previous
section was applied to the development of LKIF Core. We first describe
the building and clustering phase, followed by a discussion of the ex-
isting ontologies we considered for inclusion, and a description of the
concepts defined in the different modules of the ontology.
4.1. Ontology Capture
The LKIF Core ontology should contain ‘basic concepts of law’. It is
dependent on the (potential) users what kind of vocabulary is aimed
at. We have identified three main groups of users: citizens, legal pro-
fessionals and legal scholars. Although legal professionals use the legal
vocabulary in a far more precise and careful way than laymen, it ap-
pears that for most of these terms there is still a sufficient common
understanding to treat them more or less as similar (Lame, 2006).
Nonetheless, a number of basic terms have a specific legal-technical
meaning, such as ‘liability’ and ‘legal fact’. We included these technical
terms because they might capture the ‘essential’, abstract meaning of
terms in law, but also because these terms might be used to organise
more generally understood legal terms.
The Estrella consortium includes representatives of the three kinds
of experts. Each partner was asked to supply their ‘top-20’ of legal
The LKIF Core Ontology of Basic Legal Concepts 49
concepts. Combined with terms we collected from literature (jurispru-
dence and legal text-books) we obtained a list of about 250 terms.
As such a number is unmanageable as a basic set for modelling, we
asked partners to assess each term from this list on five scales: level
of abstraction, relevance for the legal domain, the degree to which a
term is legal rather than common-sense, the degree to which a term
is a common legal term (as opposed to a term that is specific for some
sub-domain of law), and the degree to which the expert thinks this term
should be included in the ontology. The resulting scores were used to
select an initial set of 50 terms plus those re-used from other ontologies
(see section 4.2), and formed the basis for the identification of clusters
and the development of the LKIF Core ontology.
4.2. Other Ontologies
We expected to be able to reuse terms and definitions from existing core
or upper ontologies that contain legal terms, as e.g. listed in (Casanovas
et al., 2006). Unfortunately, it turned out that the amount of re-use and
inspiration was rather limited. The following core ontologies for law were
consulted, both for their potential contribution for creating a coherent
top for LKIF Core, and specifically for legal terms already represented.
The intentional nature of the core concepts for the LKIF ontology
(see e.g. sections 4.3.2,4.3.3) emphasises the distinction with other more
(meta)physically inclined top ontologies such as SUMO5 , Sowa’s upper
ontology (Sowa, 2000) and DOLCE6 (Gangemi et al., 2002)), but shows
similarities with the distinction between intentional, design and physical
stances described in (Dennett, 1987). As some of these top- or upper
ontologies (SUMO, Sowa) do not have a common-sense basis – e.g.
mental and social entities are poorly represented – they could neither be
used as a top for LKIF Core, nor as a source of descriptions of legal terms.
The upper part of the CYC 7 ontology and DOLCE (Gangemi et al.,
2003; Massolo et al., 2002) are claimed to have a common-sense view,
but this common-sense view is rather based upon personal intuition
than on empirical evidence. LRI-Core on the other hand is to a large
extent based upon empirical studies in cognitive science, and is intended
as a core ontology for law. However, the number of typical legal concepts
in this legal core ontology is disappointingly small. Nonetheless, its top
structure appeared to be valuable in constructing LKIF as is further
described in Section 4. The Language for Legal Discourse (McCarty,
5
Suggested Upper Merged Ontology; http://ontology.teknowledge.com
6
Descriptive Ontology for Linguistic and Cognitive Engineering; http://www.
loa-cnr.it/DOLCE.html
7
www.cyc.com
50 Hoekstra, Breuker, Di Bello, Boer
legal_role
time
role action process mereology top
norm
legal_action expression place
core
time_modification
rules
Figure 1. Dependencies between LKIFCore modules.
1989, LLD) is a first attempt to define legal concepts in the context of
legal reasoning, using formulae and rules. Properly speaking, LLD is not
an ontology but a framework but it is a relatively rich source for legal
terms and their definitions. The Core Legal Ontology (CLO) is used
to support the construction of legal domain ontologies (Gangemi et al.,
2005). CLO organises legal concepts and relations on the basis of formal
properties defined in DOLCE+. Although purpose and layers are largely
similar to those of LRI-Core, the top structures differ considerably.
4.3. Ontology Modules
The list of terms and insights from the requirements-phase resulted in
a collection of ontology modules, each of which represents a relatively
independent cluster of concepts: expression, norm, process, action, role,
place, time and mereology (Breuker et al., 2006; Breuker et al., 2007).
The concepts in these clusters were formalised using OWL-DL in a
middle-out fashion: for each cluster the most central concepts were
represented first.8
Discussions, further literature study and the consideration of exist-
ing ontologies, led to an extension of the original set of clusters to 14
modules (see Figure 1), each of which describes a set of closely related
concepts from both legal and commonsense domains. Nonetheless, we
maintained the original views used to identify the clusters, as the ex-
planations and justifications are still valid and applicable to the current
version of the ontology. We can distinguish three layers in the ontology:
the top level (Section 4.3.1), the intentional level (Section 4.3.2) and
the legal level (Section 4.3.3).
4.3.1. First Things First: The top-level
The description of any legally relevant fact, event or situation requires
a basic conceptualisation of the context in which these occur: the back-
drop, or canvas, that is the physical world. Fundamental notions such as
8
We used both TopBraid Composer (http://www.topbraidcomposer.com) and
Protégé 3.2/4.0 (http://protege.stanford.edu).
The LKIF Core Ontology of Basic Legal Concepts 51
owl:Thing
Mental_Concept Occurrence Physical_Concept Abstract_Concept
Mental_Object Spatio_Temporal_Occurrence
Figure 2. Concepts defined in the Top module.
location, time, parthood and change are indispensable in a description
of even the simplest legal account. The top level clusters of the ontology
provide (primitive) definitions of these notions, which are consequently
used to define more intentional and legal concepts in other modules.
The most general classes of the LKIF ontology are borrowed from LRI
Core. We distinguish between mental, physical and abstract concepts,
and occurrences (Figure 2).
Mereological relations allow us to define parts and wholes, allow
for expressing a systems-oriented view on concepts, such as functional
decompositions, and containment (Figure 3). Furthermore, they form
the basis for definitions of places (location) and moments and intervals
in time.
The ontology for places in LKIF Core is based on the work of (Don-
nelly, 2005), and adopts a distinction between relative places and ab-
solute places, which goes back to Isaac Newton. Whereas a relative
place is defined by reference to some thing, absolute places are part
of absolute space and have fixed spatial relations with other absolute
places. See figure 3 for an overview of concepts defined in the place
module. A Location_Complex is a set of places that share a reference
location.
Of the properties defined in this module, meet is the most basic as
it is used to define many of the other properties such as abut, cover,
coincide etc. See (Breuker et al., 2007; Donnelly, 2005) for a more in
depth discussion of these and other relations. The current version of the
ontology of places does not define concepts and relations that can be
used to express direction and orientation.
Closely related to the theory of places of (Donnelly, 2005) is Allen’s
theory of time (Allen, 1984; Allen and Ferguson, 1994). We adopt
his theory, and distinguish between the basic concepts of Interval and
Moment. Intervals have an extent (duration) and can contain other
intervals and moments. Moments are points in time, they are atomic
and do not have a duration or contain other temporal occurrences (see
figure 4).
The relations between temporal occurrences are what defines time.
Like (Donnelly, 2005), (Allen, 1984) adopts the meet relation to define
two immediately adjacent temporal occurrences. We call this relation
52 Hoekstra, Breuker, Di Bello, Boer
owl:Thing
Occurrence
Location_Complex Spatio_Temporal_Occurrence spatial_reference
Abstract_Concept
location_complex
Comprehensive_Place Place Whole
part_of disjoint_with
Relative_Place
Part Composition Atom
disjoint_with
Absolute_Place Pair
Figure 3. Place and Mereology related concepts.
immediately_before, as the temporal meet relation holds only in one
direction, and is asymmetric. The property is used to define other
temporal relations such as before, after, during, etc.
With these classes and properties in hand, we introduce concepts of
(involuntary) change. The process ontology relies on descriptions of time
and place for the representation of duration and location of changes.
A Change is essentially a difference between the situation before and
after the change. It can be a functionally coherent aggregate of one or
more other changes. More specifically, we distinguish between Initiation,
Continuation and Termination changes.
Changes that occur according to a certain recipe or procedure, i.e.
changes that follow from causal necessity are Processes; they introduce
causal propagation. Contrary to changes, processes are bound in time
and space: they have duration and take place at a time and place.
We furthermore distinguish Physical_Processes which operate on Physi-
cal_Objects. Furthermore, at this level we do not commit to a particular
theory of causation or causal propagation.
4.3.2. The Intentional Level
Legal reasoning is based on a common sense model of intelligent be-
haviour, and the prediction and explanation of intelligent behaviour.
It is after all only behaviour of rational agents that can be effectively
influenced by the law. The modules at the intentional level include
concepts and relations necessary for describing this behaviour (i.e. Ac-
tions undertaken by Agents in a particular Role) which are governed by
law. Furthermore, it introduces concepts for describing the mental state
of these agents, e.g. their Intention or Belief, but also communication
between agents by means of Expressions.
The class of agents is defined as the set of things which can be
the actor of an intentional action: they perform the action and are
The LKIF Core Ontology of Basic Legal Concepts 53
...
Physical_Concept
...
... ... Physical_Object
... Spatio_Temporal_Occurrence
disjoint_with
Composition Temporal_Occurrence immediately_afterimmediately_before Temporal_Occurrence Change
resource
Pair component Interval Atom
Process Termination Continuation Initiation
disjoint_with
Pair_Of_Periods Moment Physical_Process
Figure 4. Concepts related to time and change.
potentially liable for any effects caused by the action (see figure 5).
Actions are processes, they are the changes performed by some agent
who has the intention of bringing about the change. Because actions are
processes they can become part of causal propagation, allowing us to
reason backwards from effect to agent. Actions can be creative in that
they initiate the coming into existence of some thing, or the converse.
Also, actions are often a direct reaction to some other action (see figure
5).
The agent is the medium of some intended outcome of the action:
an action is always intentional. The intention held by the agent, usu-
ally bears with it some expectation that the intended outcome will
be brought about: the agent believes in this expectation. The actions
an agent is expected or allowed to perform are constrained by the
competence of the agent, sometimes expressed as roles assigned to the
agent.
We distinguish between persons, individual agents such as “Joost
Breuker” and “Pope Benedict XVI”, and Organisations, aggregates of
other organisations or persons which acts ‘as one’, such as the “Dutch
Government” and the “Sceptics Society” . Artefacts are physical objects
designed for a specific purpose, i.e. to perform some Function as in-
strument in a specific set of actions such as “Hammer” and “Atlatl” 9 .
Persons are physical objects as well, but are not designed (though some
might hold the contrary) and are subsumed under the class of Natu-
ral_Objects. Note that natural objects can function as tools or weapons
as well, the typical example being a stone, but are not designed for that
specific purpose.
The notion of roles has played an important part in recent discus-
sions on ontology (Steimann, 2000; Masolo et al., 2004; Guarino and
9
An atlatl is a tool that uses leverage to achieve greater velocity in spear-
throwing, see http://en.wikipedia.org/wiki/Atlatl
54 Hoekstra, Breuker, Di Bello, Boer
...
Physical_Object ...
Artifact ... ... Mental_Object
disjoint_with
Natural_Object Agent Process Plan
actor part disjoint_with
Person Action Collaborative_Plan
member disjoint_with
Organisation Creation Reaction Personal_Plan Transaction
Figure 5. Actions, agents and organisations.
Welty, 2002). Roles not only allow us to categorise objects according to
their prototypical use and behaviour, they also provide the means for
categorising the behaviour of other agents. They are a necessary part
of making sense of the social world and allow for describing social or-
ganisation, prescribe behaviour of an agent within a particular context,
and recognise deviations from ‘correct’ or normal behaviour. Indeed,
roles and actions are closely related concepts: a role defines some set of
actions that can be performed by an agent, but is conversely defined
by those actions. Roles specify standard or required properties and be-
haviour (see figure 6). The role module captures the roles and functions
that can be played and held by agents and artefacts respectively, and
focuses on social roles, rather than traditional thematic or relational
roles.
A consequence of the prescriptive nature of roles is that agents
connect expectations of behaviour to other agents: intentions and ex-
pectations can be used as a model for intelligent decision making and
planning10 . It is important to note that there is an internalist and
an externalist way to use intentions and expectations. The external
observer can only ascribe intentions and expectations to an agent based
on his observed actions. The external observer will make assumptions
about what is normal, or apply a normative standard for explaining the
actions of the agent.
10
Regardless of whether it is a psychologically plausible account of decision mak-
ing. Daniel Dennett’s notion of the Intentional Stance is interesting in this context
(cf. (Dennett, 1987)). Agents may do no more than occasionally apply the stance
they adopt in assessing the actions of others to themselves.
The LKIF Core Ontology of Basic Legal Concepts 55
Mental_Concept
Role
plays
owl:Thing Function Epistemic_Role
disjoint_with
Social_Role
Figure 6. Roles.
The expression module covers a number of representational primi-
tives necessary for dealing with Propositional_Attitudes (viz. (Dahllöf,
1995)). Many concepts and processes in legal reasoning and argumen-
tation can only be explained in terms of propositional attitudes: a
relational mental state connecting a person to a Proposition. However, in
many applications of LKIF the attitude of the involved agents towards
a proposition will not be relevant at all. For instance, fraud detection
applications will only care to distinguish between potentially contra-
dictory observations or expectations relating to the same propositional
content. Examples of propositional attitudes are Belief, Intention, and
Desire. Each is a component of a mental model, held by an Agent.
Communicated attitudes are held towards expressions: propositions
which are externalised through some medium. Statement, Declaration,
and Assertion are expressions communicated by one agent to one or
more other agents. This classification is loosely based on Searle (cf.
(Searle and Vanderveken, 1985)). A prototypical example of a medium
in a legal setting is e.g. the Document as a bearer of legally binding
(normative) statements.
When propositions are used in reasoning they have an epistemic
role, e.g. as Assumption, Cause, Expectation, Observation, Reason, Fact
etc. The role a proposition plays within reasoning is dependent not only
on the kind of reasoning, but also the level of trust as to the validity
of the proposition, and the position in which it occurs (e.g. hypothesis
vs. conclusion). In this aspect, the expression module is intentionally
left under-defined. A rigourous definition of propositional attitudes re-
lates them to a theory of reasoning and an argumentation theory. The
argumentation theory is supplied by an argumentation ontology. The
theory of reasoning depends on the type of reasoning task (assessment,
design, planning, diagnosis, etc.) LKIF is used in, and should be filled
in (if necessary) by the user of LKIF .
Evaluative_Attitudes express an evaluation of a proposition with re-
spect to one or more other propositions, they express e.g. an evaluation,
a value statement, value judgement, evaluative concept, etc. I.e. only
56 Hoekstra, Breuker, Di Bello, Boer
... ...
Mental_Object Qualified qualitatively_comparable ...
qualifies
Proposition Qualification Medium
towards medium
... Evaluative_Proposition evaluatively_comparable Propositional_Attitude Expression Document
evaluates states
Creation Evaluative_Attitude Intention Belief Communicated_Attitude
creates
Speech_Act Declaration Promise Statement_In_Writing Assertion
Figure 7. Propositions, Attitudes and Expressions.
the type of qualification which is an attitude towards the thing being
evaluated, and not for instance the redness of a rose, as in (Gangemi
et al., 2002) and others. Of special interest is the Qualification, which
is used to define norms based on (Boer et al., 2005). Analogous to the
evaluative attitude, a qualification expresses a judgement. However, the
subject of this judgement need not be a proposition, but can be any
complex description (e.g. a situation).
4.3.3. The Legal Level
Legally relevant statements are created through public acts by both
natural and legal persons. The legal status of the statement is dependent
on both the kind of agent creating the statement, i.e. Natural_Person vs.
a Legislative_Body, and the rights and powers attributed to the agent
through mandates, assignments and delegations. At the legal level, the
LKIF ontology introduces a comprehensive set of legal agents and ac-
tions, rights and powers (a modified version of (Sartor, 2006; Rubino
et al., 2006)), typical legal roles, and concept definitions which allow us
to express normative statements as defined in (Boer et al., 2005; Boer,
2006; Boer et al., 2007).
The Norm is a statement combining two performative meanings: it is
deontic, in the sense that it is a qualification of the (moral or legal)
acceptability of some thing, and it is directive in the sense that it
commits the speaker to bringing about that the addressee brings about
the more acceptable thing (cf. (Nuyts et al., 2005)), presumably through
a sanction. These meanings do not have to occur together. It is perfectly
possible to attach a moral qualification to something without directing
anyone, and it is equally possible to issue a directive based on another
reason than a moral or legal qualification (e.g. a warning).
A norm applies to (or qualifies) a certain situation (the Qualified
situation), allows a certain situation – the Obliged situation or Allowed
The LKIF Core Ontology of Basic Legal Concepts 57
owl:Thing
Mental_Concept Qualified
qualifies Mental_Object Normatively_Qualified
qualifies
Qualification Norm Allowed Disallowed
allows
Permission Right Obliged
disallows
Obligation allows
equivalent_class
Prohibition
Figure 8. Qualifications and Norms
situation – and disallows a certain situation – the Prohibited or Disal-
lowed situation, see Figure 8. The obliged and prohibited situation are
both subsumed by the situation to which the norm applies. Besides that
they by definition form a complete partition of the case to which the
norm applies, i.e. all situation to which the norm applies are either a
mandated case or a prohibited situation. This is true of the obligation
and the prohibition: they are simply two different ways to put the
same thing into words. The permission is different in that it allows
something, but it does not prohibit anything. The logical complement
of the mandated situation is here an opposite qualified situation, about
which we know only that it cannot be obliged.
5. Putting the ontology to use: the Traffic domain
The LKIF ontology not only provides a theoretical understanding of
the legal domain, but its primary use in practice is as a tool to facil-
itate knowledge acquisition, exchange and representation: i.e. to for-
malise pieces of existing legislation. We evaluated the use of the ontol-
ogy by formalising the EU Directive 2006/126 on driving licences,11 ,
a relatively straightforward regulation, in which at least two types of
normative statement are recognisable—definitional and deontic.
An example of a definitional statement from the EU directive is:
Art. 4(2) Category AM: Two-wheel vehicles or three-wheel vehicles
with a maximum design speed of not more than 45 km/h.
11
The text is available on-line at http://eur-lex.europa.eu/.
58 Hoekstra, Breuker, Di Bello, Boer
The mereo module of the ontology along with a qualified cardinality
restriction (available with OWL 1.1) allows us to express that AM
vehicles have two or three wheels:
AM ⊑ 2composed_of.Wheel ⊔ 3composed_of.Wheel.
Modelling ‘design speed not more than 45 km/h’ is more challenging
as it requires us to represent the rather common sense domain of speeds,
distances etc. Of course, one could introduce the datatype property
designSpeed and require its value be expressed in km/h.This choice,
however, would not make justice of the conceptual complexity involved
in ‘design speed not more than 45 km/h’, which contains reference to
several notions: unit of measurement, number, designed speed, and a
no-more relation. In fact, ‘design speed not more than 45 km/h’ can
be rendered by imposing an linear ordering relation less-than on the
different (instances of the) subclasses of the class DesignSpeed.12 The
ordering allows us to define the class of those DesignSpeeds with a value
not exceeding some N45—i.e., ∀less-than.DesSpeed-km-h-45.
Let us now look at an example of a deontic statement:
Art. 4(2) The minimum age for category AM is fixed at 16 years.
Art. 4(2) expresses an obligation whose logical form can be rendered by
the implication:
If x is driving a AM vehicle, then x must be at least 16 years old.
To fix some terminology, the antecedent is the context to which the
obligation applies; the consequent (minus the deontic operator must)
is the content of the obligation itself (what the obligations prescribes it
ought to be the case). Consistently with this analysis, the LKIF ontology
defines obligations as classes (see Section 4.3.3).
In our case, art. 4(2) allows the situation DriverAM ⊓ DriverOld-
erThan16 and forbids DriverAM ⊓ ¬DriverOlderThan16. Suppose that the
classes DriverOlderThan16 and DriverAM have already been defined.13
To model the obligation that drives of AM vehicles must be at least
the 16 years older, we introduced the obligation-type class MinAgeAM
as follows:
12
The ordering is linear—i.e, reflexive, antisymmetric, transitive and total—since
it mirrors the ordering of the natural numbers. For whenever n ≤ m, we have that
DesignSpeed-km-h-n(a) less-than DesignSpeed-km-h-m(b), with a, b instances.
13
The class DriverOlderThan16 can be defined by using a more-than ordering re-
lation, roughly along the same lines as the class ∀less-than.DesSpeed-km-h-45. The
class DriverAM can be easily defined.
The LKIF Core Ontology of Basic Legal Concepts 59
MinAgeAM ⊑ ∀allows.(DriverAM ⊓ DriverOlderThan16).
MinAgeAM ⊑ ∃allows.(DriverAM ⊓ DriverOlderThan16).
MinAgeAM ⊑ ∀disallows.(DriverAM ⊓ ¬DriverOlderThan16).
MinAgeAM ⊑ ∃disallows.(DriverAM ⊓ ¬DriverOlderThan16).
Other deontic operators, such as permission or prohibition, can be
accounted in an alike manner (see (Boer et al., 2007)). Notwithstand-
ing the parsimony of this type of definition, using the LKIF ontology
to model normative statements proves to be rather straightforward.
Of course, a specialised modelling environment for legislative drafters
would need to provide a shorthand for such standard OWL definitions.14
The representation of art. 4(2) suggests the LKIF ontology be aug-
mented with a module taking care of quantities, units of measurement,
numbers, fractions, mathematical operations, and the like. This is cru-
cial not only for the EU Directive 2006/126, in which most definitional
statements contain quantitative features of vehicles (e.g., power, cylin-
der capacity); quantities and calculations play a central role in any
legislative text. Note, however, that the LKIF ontology can only provide
a purely terminological account, without being able to do mathemat-
ical computations. This is unavoidable, given that OWL is a purely
logical language. We are currently investigating whether we can im-
port an existing OWL ontology dealing with measurements, such as
PhysSys/SUMO or from the Ontolingua server15 .
6. Discussion
As LKIF Core was developed by a heterogeneous group of people, we
specified a number of conventions to uphold during the representation
of terms identified in the previous phases (See (Breuker et al., 2007)).
One of these is that classes should be represented using necessary &
sufficient conditions as much as possible (i.e. by means of equivalentClass
statements). Using such ‘complete’ class definitions ensures the ability
to infer the type of individuals; this does not hold for partial class
definitions (using only necessary conditions).
In retrospect, this convention turned out to pose severe problems
for existing OWL-DL and OWL 1.1 reasoners as their performance is
significantly affected by the generic concept inclusion axioms (GCI):
14
See e.g. the SEAL project, http://www.leibnizcenter.org/project/
current-projects/seal
15
See http://www.ksl.stanford.edu/software/ontolingua/
60 Hoekstra, Breuker, Di Bello, Boer
axioms where the left-hand side of a subClassOf statement is a complex
class definition. These axioms are abundant when defining classes as
equivalent to e.g. someValuesFrom restrictions and in combination with
lots of inverse property definitions, this creates a large completion graph
for DL reasoners16 As result of these findings, the LKIF ontology has
undergone a significant revision since its initial release.
Using LKIF Core in practice, as e.g. in the traffic example, points to
the traditional knowledge-acquisition bottle-neck: for any formal repre-
sentation of any domain, one needs to build formal representations of
adjoining domains. As has been said, this can be largely overcome by
including specialised foundational or domain ontologies in a represen-
tation based on the LKIF ontology provided that the quality of these
ontologies is sufficient. Depending on availability we might consider
providing a library of ‘compatible’ ontologies to users of LKIF Core. This
will be of especial use when the ontology vocabulary will be adopted
for expressing the LKIF vendor models that will be developed within
ESTRELLA.
With respect to coverage of the legal domain, the purpose of the
study outlined in Section 4.1 is more ambitious than only the selec-
tion of the most basic terms for describing law, but time and effort
constraints make it that we could only consider a small pool of refer-
ents. The list of terms will be subjected to a more rigourous empirical
study, whereby we will consult a group of legal professionals (taking
courses in legal drafting), and law students. These empirical studies are
planned in the sideline of ESTRELLA. By applying statistical cluster
analysis, we might be able to identify the properties of the scales used
(e.g. are they independent?) and whether the statistical clusters have
some resemblance to the clusters we have identified based on theoretical
considerations. The results of this analysis will be used to evaluate the
ontology compared to the requirements we identified in the previous
chapters.
The LKIF ontology is available online as separate but interdepen-
dent OWL-DL files, and can be obtained from the ESTRELLA website
at http://www.estrellaproject.org/lkif-core. This website also
provides links to online documentation and relevant literature.
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Thanks to Taowei David Wang for pointing this out, see http://lists.owldl.
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