=Paper=
{{Paper
|id=Vol-52/paper-10
|storemode=property
|title=A Conceptual Model to Facilitate Knowledge Sharing in Multi-Agent Systems
|pdfUrl=https://ceur-ws.org/Vol-52/oas01-tamma.pdf
|volume=Vol-52
}}
==A Conceptual Model to Facilitate Knowledge Sharing in Multi-Agent Systems==
https://ceur-ws.org/Vol-52/oas01-tamma.pdf
A conceptual model to facilitate knowledge sharing in
multi-agent systems
Valentina Tamma & Trevor Bench-Capon
Agent ART group,
Department of Computer Science
Chadwick Building, Peach Street
Liverpool, L69 7ZF, UK
{valli, tbc}@csc.liv.ac.uk
ABSTRACT ditionally, this paradigm provides robustness and flexibility
This paper presents and motivates an extended ontology of the interfaces between both the agents that exist within
knowledge model which represents semantic information about the Internet and between agents and software systems, this
concepts explicitly. This knowledge model results from en- is essential since the interfaces cannot be anticipated at de-
riching the standard conceptual model with semantic infor- sign time.
mation which precisely characterises the concept’s proper- Within a multi-agent system, agents are characterised by
ties and expected ambiguities, including which properties different ”views of the world” that are explicitly defined by
are prototypical of a concept and which are exceptional, the ontologies, that is views of what the agent knows to be the
behaviour of properties over time and the degree of applica- concepts describing application domain which is associated
bility of properties to subconcepts. This enriched conceptual with the agent together with their relationships and con-
model permits a precise characterisation of what is repre- straints [3]. The interoperability typical of multi-agent sys-
sented by class membership mechanisms and helps knowl- tems is achieved through the reconciliation of these views of
edge engineers to determine, in a straightforward manner, the world by a commitment to common ontologies that per-
the meta-properties holding for a concept. Meta-properties mit agents to interoperate and cooperate while maintaining
are recognised to be the main tool for a formal ontological their autonomy.
analysis that allows building ontologies with a clean and un- In open systems, agents are associated with knowledge sources
tangled taxonomic structure. which are diverse in nature and have been developed for dif-
This enriched semantics can prove useful to describe what ferent purposes. Knowledge sources embedded in a dynamic
is known by agents in a multi-agent systems, as it facilitates environment can join and leave the system at any time.
the use of reasoning mechanisms on the knowledge that in- From the ontologies perspective dealing with open systems
stantiate the ontology. These mechanisms can be used to implies that ontologies are often the efforts of many domain
solve ambiguities that can arise when heterogeneous agents experts and are designed and maintained independently in
have to interoperate in order to perform a task. distributed environments. In such a situation interopera-
tion between agents is based on the reconciliation of their
heterogeneous views, which is accomplished by merging or
1. INTRODUCTION integrating the diverse ontologies associated with the agents
Advances in the Internet have made it possible to access composing the system [27]. The merging and integration of
huge amounts of diverse information from different places diverse ontologies has to be accomplished bearing in mind
all over the world. This possibility has stimulated a grow- that since agents are highly heterogeneous, they are likely to
ing demand for understanding how to integrate multiple and be incapable to fully understand each other, therefore both
heterogeneous knowledge sources in order to provide added syntactic and semantic inconsistencies can arise and thus
value. The complexity of this task is quite high, chiefly be- need to be reconciled.
cause of the heterogeneity of the knowledge sources and, to Agent’s ability to represent domain knowledge in a consis-
a limited extent, of their size. tent manner has to be complemented by some reasoning
One knowledge engineering paradigm that has proved to capability. According to Wooldridge and Jennings, [31] an
be useful for dealing with the integration of heterogeneous agent architecture is one that contains an explicitly repre-
knowledge is based on a multi-agent system architecture, sented, symbolic model of the world. and in which decisions
where human and software agents interoperate and so coop- (for example about what action to perform) are made via log-
erate within common application areas. Agents in a multi- ical (or at least pseudo-logical) reasoning, based on pattern
agent system are characterised by abstraction, interoperabil- matching and symbolic manipulation. Therefore ontologies
ity, modularity and dynamism. These qualities are particu- in multi-agent systems require a high degree of expressive
larly useful in that they can help to promote open systems power to support the application of reasoning techniques
which are typically dynamic, unpredictable and highly het- that result in sophisticated inferences such as those used
erogeneous [14], as is the Internet. In these types of applica- in negotiation, which is motivated by the requirement for
tion domains, the interoperability offered by the multi-agent agents to solve problems arising from their interdependence
system approach is required because the individual compo- upon one another. [19]
nents that interact with agents are not known a priori. Ad-
Designing multi-agent systems to deal with the sharing of and which properties change over time. This dynamic be-
heterogeneous knowledge sources gives rise to the require- haviour also forms part of the domain conceptualisation and
ment for ontologies that can be easily integrated and pro- can help to identify the meta-properties holding for the con-
vide a base for applying reasoning mechanisms, highlighting cept.
the importance of suitable conceptual models for ontologies. From the multi-agent system perspective, we wish to pro-
Indeed, it has been made a point that the sharing of ontolo- vide a better characterisation, and thus understanding of
gies depends heavily on a precise semantic representation of the concepts that are known to an agent. Understanding
the concepts and their properties [4, 16, 28]. which concepts are associated with an agent and the prop-
This paper presents and motivates a knowledge model for erties holding for each concept becomes extremely impor-
ontologies which extends the usual set of facets in the OKBC tant when agents need to agree on one or more common
frame-base model [2] to encompass more semantic informa- shared ontologies, where each shared concept is obtained
tion concerning the concept, to give of a precise characteri- as reconciliation of the local views. Describing concepts by
sation of the concept’s properties and expected ambiguities: characterising the behaviour of their properties allow incon-
these include which properties are prototypical of a concept sistencies while integrating and reasoning that have to be
and which are exceptional; the behaviour of the property dealt with, as illustrated is the next two subsections.
over time and the degree of applicability of properties to
subconcepts. This enriched knowledge model aims to pro-
vide enough semantic information to deal with problems of
2.2 Integrating diverse ontologies
The second argument concerns the integration of the di-
semantic inconsistency that arise when reasoning with inte-
verse agent views, which is accomplished by integrating the
grated ontologies.
ontologies associated with the agents.
The paper is organised as follows: section 2 presents the
Integrating ontologies involves identifying overlapping con-
motivations for adding semantics to the conceptual model,
cepts and creating a new concept, usually by generalising
section 3 presents the enriched knowledge model while in
the overlapping ones, that has all the properties of the orig-
section 4 the model is discussed with respect to the motiva-
inals and so can be easily mapped into each of them. Newly
tions. Section 5 discusses the representation of roles using
created concepts inherit properties, usually in the form of
the knowledge model and section 6 provides an example of
attributes, from each of the overlapping ones. That is, let
concept description using the knowledge model. Finally, in
us suppose that the concept C is present in n ontologies
section 7 conclusions are drawn and future research direc-
O1 , O2 , · · · , On , although described by different properties.
tions are illustrated in section 8.
That is each ontology Oi , i = 1, · · · , n defines a concept
Ci , i = 1, · · · , n such that C1 ≈ C2 ≈ · · · ≈ Cn (where ≈
2. ENCOMPASSING SEMANTICS IN THE denotes that the concepts are overlapping). Each concept
CONCEPTUAL MODEL Ci , i = 1, · · · , n is described in terms of a set of properties
The motivation for enriching semantically the ontology con- PiC , i = 1, · · · , n. The result of the integration of the n on-
ceptual model draws on three distinct arguments that are tologies is another ontology defining the concept Cintegrated
S
n
analysed in the reminder of this section. which is defined in terms of PiC , where all the PiC have
i=1
to be distinguished.
2.1 Nature of ontologies One of the key points for integrating diverse ontologies is
The first argument is based on the nature of ontologies. It providing methodologies for building ontologies whose taxo-
has been argued that an ontology is ”an explicit specifica- nomic structure is clean and untangled in order to facilitate
tion of a conceptualisation” [8]. In other words an ontology the understanding, comparison and integration of concepts.
explicitly defines the type of concepts used to describe the Several efforts are focusing on providing engineering prin-
abstract model of a phenomenon and the constraints on their ciples to build ontologies, for example [6, 7]. Another ap-
use. [26]. An ontology is an a priori account of the objects proach [11, 12] concentrates on providing means to perform
that are in a domain and the relationships modelling the an ontological analysis which gives prospects for better tax-
structure of the world seen from a particular perspective. onomies. This analysis is based on on a rigorous analysis of
In order to provide such an account one has to understand the ontological meta-properties of taxonomic nodes, which
the concepts that are in the domain, and this involves a are based on the philosophical notions of unity, identity,
number of things. It involves knowing what can be sensi- rigidity and dependence [13].
bly said of a thing falling under a concept. This can be When the domain knowledge associated with different agents
represented by describing concepts in terms of their proper- needs to be integrated, inconsistencies can become evident.
ties, and by giving a full characterisation of these properties. Many types of ontological inconsistencies have been defined
Thus, when describing the concept Bird it is important to in the literature, for instance in [30] and there are ontol-
distinguish that some birds fly and others do not. A full un- ogy environments currently available that try to deal with
derstanding of a concept involves more than this, however: these inconsistencies, such as smart [4] and Chimaera [17].
it is important to recognise which properties are prototypical Here we broadly classify inconsistencies in ontologies into
[20] for the class membership and, more importantly, which two types: structural and semantic. We define structural
are the permitted exceptions. There are, however, differ- inconsistencies as those that arise because of differences in
ences in how confident we can be that an arbitrary member the properties that describe a concept. Structural incon-
of a class conforms to the prototype: it is a very rare mam- sistencies can be detected and resolved automatically with
mal that lays eggs, whereas many types of well known birds limited intervention from the domain expert. For example,
do not fly. a concept C can be defined in two different ontologies O1
Understanding a concept also involves understanding how and O2 in terms of an attribute A that is specified as tak-
ing values in two different domains D1 in O1 and D2 in O2 , - Nixon→ Quaker ;
where D1 ⊆ D2 . Structural inconsistencies can be detected
and resolved automatically with limited intervention from - Quaker→ Pacifist ;
the domain expert.
Semantic inconsistencies are caused by the knowledge con- - Republican→ Hawk ;
tent of diverse ontologies which differs both in semantics
and in level of granularity of the representation. They af- The two concepts Quaker and Republican are described
fect those attributes that are actually representing concept by two attributes Pacifist and Hawk that have different
features and not relations with other concepts. Semantic names but are semantically related (one is the opposite of
inconsistencies require a deeper knowledge on the domain. the other), as they both describe someone’s attitude towards
Examples of semantic inconsistencies can be found in [17, going to war. In this case extra semantic information on the
28]. Adding semantics to the concept descriptions can be properties, such as the extent to which the property applies
beneficial in solving this latter type of conflict, because a to the members of the class, can be used to derive which
richer concept description provides more scope to resolve property is more likely to apply to the situation at hand.
possible inconsistencies. Of course, such sophisticated assumptions cannot always be
made automatically and might need to be validated by the
2.3 Reasoning with ontologies system user or by some other agent.
The last argument to support the addition of semantics to
ontology conceptual models turns on the need to reason with
the knowledge expressed in the ontologies.
3. EXTENDED KNOWLEDGE MODEL
In this section we extend the OKBC knowledge model [?].
We have already mentioned that one of the important prob-
This knowledge model is based on classes, slots, and facets.
lems to be solved when building agents is the representa-
Classes correspond to concepts and are collections of objects
tion/reasoning problem, [31] that is:
sharing the same properties, hierarchically organised into a
multiple inheritance hierarchy, linked by IS-A links. Classes
how to symbolically represent information about are described in terms of slots, or attributes, that can either
real world entities and processes, and how to get be sets of single values. A slot is described by a name, a
agents to reason with this information in time for domain, a value type and by a set of additional constraints,
the result to be useful. here called facets. Facets can contain the documentation for
a slot, constrain the value type or the cardinality of a slot,
From the ontology perspective the reasoning aspect of the and provide further information concerning the slot and the
representation/reasoning problem involves the ability of rea- way in which the slot is to be inherited by the subclasses.
soning with the knowledge obtained by integrating or merg- In the following small example, that will be used throughout
ing diverse ontologies. Indeed, when ontologies are inte- the paper to illustrate the knowledge model here provided,
grated, new concepts are created from the definitions of the we start by describing a concept using the basic information
existing ones. In such a case conflicts can arise when con- provided by a frame-based knowledge model. The example
flicting information is inherited from two or more general is taken from the medical domain and we have chosen to
concepts and one tries to reason with these concepts. Inher- model the concept of blood pressure. Blood pressure is rep-
iting conflicting properties in ontologies is not as problem- resented here as an ordered pair (s, d) where s is the value
atic as inheriting conflicting rules in knowledge bases, since of the systolic pressure while d is the value of the diastolic
an ontology is only providing the means for describing explic- pressure.
itly the conceptualisation behind the knowledge represented Classes are denoted by the label c, slots by the label s and
in a knowledge base [1]. Thus, in a concept description con- facets by the label f. We could describe the concept as:
flicting properties can coexist. However, when one needs to
reason with the knowledge in the ontology, conflicting prop- c: Circulatorysystem;
erties can hinder the reasoning process. Furthermore, if the s: Bloodpressure
ontologies one wants to reason with have been developed f: Domain: [(0,0)-(300,200)];
at different times and for diverse purposes, it is likely that f: Value: [(90,60)-(130,85)];
problem of implicit inconsistencies will arise. This kind of
problem is quite similar to the semantic inconsistencies that where, for example, the value [(90,60)-(130,85)] means that
have been defined in section 2.2. Such a problem has been usually the minimum systolic pressure is 90 and the min-
first identified in the inheritance literature [18] where Mor- imum diastolic pressure is 60 while the maximum systolic
genstern distinguishes explicit from the implicit inconsisten- pressure is 130 and the maximum diastolic pressure is 85.
cies ones. Explicit inconsistencies arise when two concepts In the extended knowledge model that we propose the set of
Ci and Cj are described in terms of explicitly conflicting facets has been extended from that provided by OKBC [2]
properties, that is in terms of the same attribute which is in order to encompass descriptions of the attribute and its
associated with conflicting values V and ¬V . Implicit incon- behaviour in the concept description and changes over time.
sistencies arise when the properties are described by different The facets we use are listed below and discussed in the next
attributes but with opposite meanings. Morgenstern [18] has section:
modified the (notorious) Touretzky’s Nixon diamond [29] to
show an example of implicit inconsistencies. Let us consider: • Value: It associates a value v ∈ Domain with an
attribute in order to represent a property. However,
- Nixon→ Republican ; when the concept that is defined is very high in the
hierarchy (so high that any conclusion as to the at- by the knowledge engineers while filling the slots. It
tribute’s value is not possible), then either Value = should give an account of information such as why the
Domain or Value = Subdomain⊂ Domain; ranking has been set to a specific value or what is
the context associated with a prototype (see below the
• Type of value: The possible fillers for this facet are discussion concerning prototypes). It is added to keep
Prototypical, Inherited, Distinguishing. An attribute’s track of the process leading to the modelling decisions.
value is Prototypical if the value is true for any pro-
totypical instance or the concept, but exceptions are
permitted with a degree of softness expressed by the 4. RELATING THE EXTENDED KNOWL-
facet Ranking. An attribute’s value can be Inherited EDGE MODEL TO THE MOTIVATIONS
from some super concept or it can be a Distinguishing The knowledge model presented in the previous section is
value, that is a value that differentiates among siblings. motivated by the the problems described in section 3. It is
Note that distinguishing values become inherited val- based on an enriched semantics that aims to provide a bet-
ues for subclasses of the class; ter understanding of the concepts and their properties by
characterising their behaviour.
• Exceptions: It can be either a single value or a sub- Concept properties are to be considered on three levels: in-
set of the domain. It indicates those values that are stance level, class-membership level and meta level. Proper-
permitted in the concept description because in the ties at instance level are those exhibited by all the instances
domain, but deemed exceptional from a common sense of a concept. They might specialise properties at class-
viewpoint. The exceptional values are not those which membership level, which instead describe properties holding
differ from the prototypical ones but any value which for the class. Properties at meta level have been mainly de-
is possible but highly unlikely; scribed in philosophy, such as identity, unity, rigidity and
• Ranking: An integer describing the degree of con- dependency. The proposed model permits the characterisa-
fidence of the fact that the attribute takes the value tion of concepts on the three distinct property levels, thus
specified in the facet Value. It describe the class mem- also considering the meta level which is the basis for the on-
bership condition. The possible values are 1: All, 2: tological analysis illustrated in [12]. Such an enriched model
Almost all, 3: Most, 4: Possible, 5: A Few, 6: Almost helps to characterise and identify the meta properties hold-
none, 7: None. For example, in the description of the ing for the concepts, thus providing knowledge engineers
concept Bird the slot Ability to Fly takes value Yes developing the ontologies with an aid to perform the onto-
with Ranking 3, since there are many types of birds logical analysis which is usually demanding to perform.
that do not fly. Associating a degree of confidence with Furthermore, the enriched knowledge model forces knowl-
a pair (Attribute, Value) is also an arbitrary process edge engineers to make ontological commitments explicit.
that depends on the way in which the knowledge en- Indeed, real situations are information-rich complete events
gineers writing the ontology perceive the domain. By whose context is so rich that, as it has been argued by Searle
giving 7 possibilities to fill the slot ranking we aim to [22], it can never be fully specified. Many assumptions about
provide knowledge engineers with the possibility to ex- meaning and context are usually made when dealing with
press with more detail their perception of the domain; real situations [21]. These assumptions are rarely formalised
when real situations are represented in natural language
• Change frequency: Its possible values are: Regular, but they have to be formalised in an ontology since they
Once only, Volatile, Never. This facet describes how are ontological commitments that have to be made explicit.
often an attribute’s value changes. If the information is Enriching the semantics of the attribute descriptions with
set equal to Regular it means that the process is contin- things such as the behaviour of attributes over time or how
uous (see section below), for instance the age of a per- properties are shared by the subclasses makes some of the
son can be modelled as changing regularly; if set equal more important assumptions explicit.
to Once only it indicates that only one change is possi- The enriched semantics is essential to solve the inconsisten-
ble, for example a person’s date of birth changes only cies that arise either while integrating diverse ontologies or
once. If the slot is set equal to Never it means that the while reasoning with the integrated knowledge. By adding
value associated with the attribute cannot change, and information on the attributes we are able to better measure
finally Volatile indicates that the attribute’s value can the similarity between concepts, to disambiguate between
change more than once, for example people a person’s concepts that seem similar while they are not, and we have
blood pressure can change several times, both because means to infer which property is likely to hold for a concept
of the aging process and because of specific events such that inherits inconsistent properties. The remainder of this
as chock or diseases; section describes the additional facets and relates them to
the discussion in section 5.
• Event: Describes conditions under which the value
changes. It is the set {((Ej , Sj , Vj ), Rj )|j = 1, · · · , m}
where Ej is an event, Sj is the state of the pair attribute- 4.1 Behaviour over time
value associated with a property, Vj defines the event In the knowledge model the facets Change frequency and
validity and Rj denotes whether the change is reversible Event describe the behaviour of properties over time, which
or not. The semantics of this facet is explained in the models the changes in properties that are permitted in the
section below; concept’s description without changing the essence of the
concept. The behaviour over time is closely related to estab-
• Documentation: This is not strictly speaking a facet, lishing the identity of concept descriptions [12]. Describing
but a string that is add to document the choices made the behaviour over time involves also distinguishing proper-
ties whose change is reversible from those whose change is a property that is essential to all its instances,
irreversible. i.e. ∀xφ(x) → 2φ(x).
Property changes over time are caused either by the natural
passing of time or are triggered by specific event occurrences.
We need, therefore, to use a suitable temporal framework The interpretation that is usually given to rigidity is that if
that permits us to reason with time and events. The model x is an instance of a concept C than x has to be an instance
chosen to accommodate the representation of the changes of C in every possible world. Time can be seen as one of
is the Event Calculus [15]. Event calculus deals with local these systems of possible worlds and characterising a prop-
event and time periods and provides the ability to reason erty as rigid in time gives a better angle on the necessary
about change in properties caused by a specific event and and sufficient conditions for the class membership.
also the ability to reason with incomplete information.
Changes of properties can be modelled as processes [24]. 4.2 Ranking
Processes can be described in terms of their starting and Rankings are defined as [5]:
ending points and of the changes that happen in between.
We can distinguish between continuous and discrete changes,
the former describing incremental changes that take place Each world is ranked by a non-negative inte-
continuously while the latter describe changes occurring in ger representing the degree of surprise associated
discrete steps called events. Analogously we can define con- with finding such a world.
tinuous properties those changing regularly over time, such
as the age of a person, versus discrete properties which
We have borrowed the term to denote the degree of sur-
are characterised by an event which causes the property
prise in finding a world where the property P holding for
to change. If the value associated with change frequency
a concept C does not hold for one of its subconcepts C 0 .
is Regular then the process is continuous, if it is Volatile
The additional semantics encompassed in this facet is im-
the process is discrete and if it is Once only the process is
portant to reason with statements that have different de-
considered discrete and the triggering event is set equal to
grees of credibility. Indeed there is a difference in asserting
time-point=T.
facts such as ”Mammals give birth to live young” and ”Bird
Any regular occurrence of time can be, however, expressed
fly”, the former is generally more believable than the latter,
in form of an event, since most of the forms of reasoning
for which many more counterexamples can be found. The
for continuous properties require discrete approximations.
ability to distinguish facts whose credibility holds with dif-
Therefore in the knowledge model presented in previous sec-
ferent degrees of strength is related to finding facts that are
tion, continuous properties are modelled as discrete proper-
true in every possible world and therefore constitute neces-
ties where the event triggering the change in property is
sary truth. The concept of necessary truth brings us back
the passing of time from the instant t to the instant t0 .
to establishing whether a property is rigid or not. In fact it
Each change of property is represented by a set of quadru-
can be assumed that the value associated with the Ranking
ples {((Ej , Sj , Vj ), Rj )|j = 1, · · · , m} where Ej is an event,
facet together with the temporal information on the changes
Sj is the state of the pair attribute-value associated with
permitted for the property lead us to determine whether the
a property, Vj defines the event validity while Rj indicates
property described by the slot is a rigid one. Rigid proper-
whether the change in properties triggered by the event Ej
ties have often been interpreted as essential properties (i.e.,
is reversible or not. The model used to accommodate this
a property holding for an individual in every possible cir-
representation of the changes adds reversibility to Event Cal-
cumstance in which the individual exists), but, we note that
culus, where each triple (Ej , Sj , Vj ) is interpreted either as
a property might be essential to a member of a class without
the concept is in the state Sj before the event Ej happens or
being essential for membership in that class. For example,
the concept is in the state Sj after the event Ej happens de-
being odd is an essential property of the number 5, but it is
pending on the value associated with Vj . The interpretation
not essential for membership in the class of prime numbers.
is obtained from the semantics of the event calculus, where
The ability to evaluate the degree of credibility of a property
the former expression is represented as Hold(before(Ej , Sj ))
in a concept description is also related to the problem of en-
while the latter as Hold(after(Ej , Sj )).
abling agents to reasoning with ontologies obtained through
The idea of modelling the permitted changes for a property
integration. In such a case, as mentioned in section 2.3,
is strictly related to the philosophical notion of identity. In
inconsistencies can arise if a concepts inherits conflicting
particular, the knowledge model addresses the problem of
properties. In order to be able to reason with these conflicts
modelling identity when time is involved, namely identity
some assumptions have to be made, concerning on how likely
through changes, which is based on the common sense no-
it is that a certain property holds; the facet Ranking mod-
tion that an individual may remain the same while show-
els this information by modelling a qualitative evaluation of
ing different properties at different times [11]. The knowl-
how subclasses inherit the property. This estimate repre-
edge model we propose explicitly distinguishes the proper-
sents the common sense knowledge expressed by linguistic
ties that can change from those which cannot, and describes
quantifiers such as All, Almost all, Few, etc..
the changes in properties that an individual can be sub-
In case of conflicts the property’s degree of credibility can be
jected to, while still being recognised as an instance of a
used to rank the possible alternatives following an approach
certain concept.
similar to the non-monotonic reasoning one developed by [5]:
The notion of changes through time is also important to
in case of more conflicting properties holding for a concept
establish whether a property is rigid. A rigid property is
description, properties are ordered according to the degree
defined in [10] as:
of credibility, that is according to the the filler associated
with the Ranking facet weighted by the Degree of strength.
Therefore, a property holding for all the subclasses is con- mally thought to be a feature of the cognitive category and
sidered to have a higher rank than one holding for few of not only what differs from the prototype.
the concept subclasses, but this ordering is adjusted by the Also the information on prototype and exceptions can prove
relevance, as perceived by the knowledge engineer, of the useful in dealing with inconsistencies arising from ontology
property in the concept’s description (Degree of strength). integration. When no specific information is made available
For example, to reason about birds ability to fly, the at- on a concept and it inherits conflicting properties, then we
tribute species is more relevant than the attribute feather can assume that the prototypical properties hold for it.
colour. When reasoning with diverse ontologies, the Degree The inclusion of prototypes in the knowledge model provides
of strength represents the weight associated with the inher- the grounds for the semi-automatic maintenance and evolu-
itance rule corresponding to the attribute. tion of ontologies by applying techniques developed in other
Although this ordering of the conflicting properties needs to fields such as machine learning.
be validated by the user, it reflects the common sense as-
sumption that, when no specific information is known, peo- 5. PROSPECTS FOR SUPPORTING ROLES
ple assume that the most likely property holds for a concept. The notion of role is central to any modelling activities as
Here we assume that the agents reflect the common sense much as those of objects and relations. A thorough discus-
reasoning that is typically human. sion of roles goes beyond the scope of this paper, and roles
are not supported yet in the knowledge model introduced in
4.3 Prototypes and exceptions section 3. However, the extended semantics provided by the
In order to get a full understanding of a concept it is not knowledge model presented above gives good prospects for
sufficient to list the set of properties generally recognised as supporting roles. In this section we provide some prelimi-
describing a typical instance of the concept but we need to nary consideration and relate the additional facets with the
consider the expected exceptions. Here we partially take the main features of the role notion.
cognitive view of prototypes and graded structures, which is Despite its importance, highlighted in the literature [9, 23],
also reflected by the information modelled in the facet Rank- few modelling languages permit the distinction between a
ing. In this view all cognitive categories show gradients of concept and the roles it can play in the knowledge model.
membership which describe how well a particular subclass This difficulty is partially due to the lack of a single defini-
fits the standard idea or image of the category to which tion for role.
the subclass belongs [20]. Prototypes are the subconcepts A definition of role that makes use of the formal meta-
which best represent a category, while exceptions are those properties and includes also the definition given by Sowa
which are considered exceptional although still belonging to [23] is provided by Guarino and Welty. In [11] they define a
the category. In other words all the sufficient conditions for role as:
class membership hold for prototypes. For example, let us
consider the biological category mammal : a monotreme (a properties expressing the part played by one en-
mammal who does not give birth to live young) is an ex- tity in an event, often exemplifying a particular
ample of an exception with respect to this attribute. Proto- relationship between two or more entities. All
types depend on the context; there is no universal prototype roles are anti-rigid and dependent... A property
but there are several prototypes depending on the context, φ is said to be anti-rigid if it is not essential to
therefore a prototype for the category mammal could be cat all its instances, i.e. ∀xφ(x) → ¬2φ(x)... A
if the context taken is that of pets but it is lion if the as- property φ is (externally) dependent on a prop-
sumed context is circus animal. Ontologies typically presup- erty ψ if, for all its instances x, necessarily some
pose context and this feature is a major source of difficulty instance of ψ must exist, which is not a part nor
when merging them. a constituent of x, i.e. ∀x2(φ(x) → ∃yψ(y) ∧
For the purpose of building ontologies for multi-agent sys- ¬P (y, x) ∧ ¬C(y, x)).
tems, distinguishing the prototypical properties from those
describing exceptions increases the expressive power of the
description. Such distinctions do not aim at establishing In other words a concept is a role if its individuals stand in
default values but rather to guarantee the ability to reason relation to other individuals, and they can enter or leave the
with incomplete or conflicting concept descriptions. extent of the concept without losing their identity. From this
The ability to distinguish between prototypes and excep- definition it emerges that the ability of recognising whether
tions helps to determine which properties are necessary and rigidity holds for some property φ is essential in order to
sufficient conditions for concept membership. In fact a prop- distinguish whether φ is a role.
erty which is prototypical and that is also inherited by all In [25] Steimann presents a list of the features that have
the subconcepts (that is it has the facet Ranking set to been associated in the literature with roles. Some of these
All ) becomes a natural candidate for a necessary condition. features are conflicting and, as pointed out, no integrating
Prototypes, therefore, describe the subconcepts that best definition has been made available. However, from the differ-
fit the cognitive category represented by the concept in the ent definitions available, it can be derived that the notion of
specific context given by the ontology. On the other hand, role is inherently temporal, indeed roles are acquired and re-
by describing which properties are exceptional, we provide a linquished in dependence either of time or of a specific event.
better description of the class membership criteria in that it For example the object person acquires the role teenager if
permits to determine what are the properties that, although the person is between 11 and 19 years old, whereas a person
rarely hold for that concept, are still possible properties de- becomes student when they enroll for a degree course. More-
scribing the cognitive category. Here, the term exceptional over, from the list of features in [25] it emerges that many of
is used to indicate something that differs from what is nor- the characteristics of roles are time or event related, such as:
an object may acquire and abandon roles dynamically, may itly representing additional information on the slot proper-
play different roles simultaneously, or may play the same ties. This knowledge model results from a conceptual model
role several time, simultaneously, and the sequence in which which encompasses semantic information aiming to charac-
roles may be acquired and relinquished can be subjected to terise the behaviour of properties in the concept descrip-
restrictions. tion. We have motivated this enriched conceptual model by
For the aforementioned reasons ways of representing roles identifying three main categories of problems that can arise
must be supported by some kind of time and event explicit in heterogeneous multi-agent systems and that can hinder
representation. We believe that the knowledge model we the communication between agents and we have shown that
have presented, although it does not encompass roles yet, these problems require additional semantics in order to be
provides sufficient semantics to model the dynamic features dealt with.
of roles, thanks to the explicit representation of time inter- The novelty of this extended knowledge model is that it
vals which is used to model the attributes behaviour over explicitly represents the behaviour of attributes over time
time. Furthermore, the ability of modelling events, used to by describing the permitted changes in a property that are
describe the possible causes in the state of an attribute, can permitted for members of the concept. It also explicitly rep-
be used to model the events that constrain the acquisition resents the class membership mechanism by associating with
or the relinquishment of a role. each slot a qualitative quantifier representing how proper-
ties are inherited by subconcepts. Finally, the model does
6. A MODELLING EXAMPLE not only describe the prototypical properties holding for a
We are now ready to complete the example by modelling the concept but also the exceptional ones.
concept blood pressure with the enriched knowledge model We have also related the extended knowledge model to the
presented above. In modelling the concept of blood pressure formal ontological analysis by Guarino and Welty [12] which
we take into account that both the systolic and diastolic permits to build ontologies that have a cleaner taxonomic
pressure can range between a minimum and a maximum structure and so gives better prospects for maintenance and
value but that some values are more likely to be registered integration. Such a formal ontological analysis is usually dif-
than others. Within the likely values we then distinguish the ficult to perform and we believe our knowledge model can
prototypical values, which are those registered for a healthy help knowledge engineers to determine the meta-properties
individual whose age is over 18, and the exceptional ones, holding for the concept by forcing them to make the onto-
which are those registered for people with pathologies such logical commitments explicit.
as hypertension or hypotension. The prototypical values A possible drawback of this approach is the high number of
are those considered normal, but they can change and we facets that need to filled when building ontology. We realise
describe also the permitted changes and what events can that this can make building an ontology from scratch even
trigger such changes. Prototypical pressure values usually more time consuming but we believe that the outcomes in
change with age, but they can be altered depending on some terms of better understanding of the concept and the role
specific events such as shock and haemorrhage (causing hy- it plays in a context together with the guidance in deter-
potension) or thrombosis and embolism (causing hyperten- mining the meta-properties at least balances the increased
sion). Also conditions such as pregnancy can alter the nor- complexity of the task.
mal readings.
Classes are denoted by the label c, slots by the label s and 8. FUTURE WORK
facets by the label f. Irreversible changes are denoted by I The extension of the knowledge model with with additional
while reversible property changes are denoted by R. semantics opens several new research directions. Firstly, the
role representation needs to be formalised in the knowledge
c: Circulatorysystem; model in order to represent also the roles hierarchical organ-
s: Bloodpressure isation [25].
f: Domain: [(0,0)-(300,200)]; We also plan to use the semantics encompassed in the knowl-
f: Value: [(90,60)-(130,85)]; edge model to assist knowledge engineers in the tasks of
f: Typeofvalue: prototypical; merging and reasoning with diverse ontologies. To reach
f: Exceptions: [(0,0)-(89,59)] ∪ [(131,86)-(300,200)]; this goal we intent to introduce some form of temporal rea-
f: Ranking: 3; soning based on the event logics that is used extend the
f: Changefrequency: Volatile; facets.
f: Event: (Age=60,[(0,0)-(89,59)] ∪ The description of attributes in terms of prototypical values
∪ [(131,86)-(300,200)],after, I); gives us the possibility of exploring the application of ma-
f: Event: (haemorrhage,[(0,0)-(89,59)],after, R); chine learning techniques to dynamically extend ontologies.
f: Event: (shock,[(0,0)-(89,59)],after, R);
f: Event: (thrombosis,[(131,86)-(300,200)],after,R); Acknowledgement
f: Event: (embolism,[(131,86)-(300,200)],after,R); The PhD research presented in this paper was funded by BT
f: Event: (pregnancy,[(0,0)-(89,59)] ∪ plc. The authors are grateful to Ray Paton for providing the
∪ [(131,86)-(300,200)],after,R); example. This paper is supported by HP.
7. CONCLUSIONS 9. REFERENCES
This paper has presented an ontology model that supports [1] A. Bernaras, I. Laresgoiti, and J. Corera. Building and
knowledge sharing in multi-agent systems where the agents reusing ontologies for electrical network applications.
can be heterogeneous. The proposed model extends the In Proceedings of the 12th European Conference on
usual ontology frame-based model such as OKBC by explic- Artificial Intelligence (ECAI), pages 298–302, 1996.
[2] V. Chaudhri, A. Farquhar, R. Fikes, P. Karp, and [16] D. McGuinness. Conceptual modelling for distributed
J. Rice. OKBC: A programmatic foundation for ontology environments. In Proceedings of the Eighth
knowledge base interoperability. In Proceedings of the International Conference on Conceptual Structures
Fifteenth American Conference on Artificial Logical, Linguistic, and Computational Issues (ICCS
Intelligence (AAAI-98), pages 600–607, Madison, 2000), 2000.
Wisconsin, 1998. AAAI Press/The MIT Press.
[17] D. McGuinness, R. Fikes, J. Rice, and S. Wilder. An
[3] S. Falasconi, G. Lanzola, and M. Stefanelli. Using environment for merging and testing large ontologies.
ontologies in multi-agent systems. In Proceedings of In Proceedings of KR-2000. Principles of Knowledge
Tenth Knowledge Acquisition for Knowledge-Based Representation and Reasoning. Morgan-Kaufman,
Systems Workshop (KAW), 1996. 2000.
[18] L. Morgenstern. Inheritance comes of age: Applying
[4] N. Fridman Noy and M. Musen. SMART: Automated nonmonotonic techniques to problems in industry.
support for ontology merging and alignment. In Artificial Intelligence, 103:1–34, 1998.
Proceedings of the 12th Workshop on Knowledge
Acquisition, Modeling and Management (KAW), [19] S. Parsons, C. Sierra, and N. Jennings. Agent that
Banff, Canada, 1999. reason and negotiate by arguing. Journal of Logic and
Computation, 8(3):261–292, 1998.
[5] M. Goldszmidt and J. Pearl. Qualitative probabilistic [20] E. Rosch. Cognitive representations of semantic
for default reasoning, belief revision, and causal categories. Journal of Experimental Psychology:
modelling. Artificial Intelligence, 84(1-2):57–112, 1996. General, 104:192–233, 1975.
[6] A. Gómez-Pérez. Knowledge sharing and reuse. In [21] E. Rosch. Reclaiming concepts. Journal of
J. Liebowitz, editor, The Handbook of Applied Expert Consciousness Studies, 6(11-12):61–77, 1999.
Systems. CRC Pres LLC, 1998.
[22] J. Searle. Intentionality. Cambridge University Press,
[7] A. Gómez-Pérez. Ontological engineering: A state of Cambridge, 1983.
the art. Expert Update, 2(3):33–43, Autumn 1999. [23] J. Sowa. Conceptual Structures: Information
Processing in Mind and Machine. Addison-Wesley,
[8] T. R. Gruber. A translation approach to portable 1984.
ontology specifications. Knowledge Acquisition,
5(2):199–220, 1993. [24] J. Sowa. Knowledge Representation: Logical,
Philosophical, and Computational Foundations.
[9] N. Guarino. Concepts, attributes and aritrary Brooks Cole Publishing Co., Pacific Grove, CA, 2000.
relations. Data and Knowledge Engineering, [25] F. Steimann. On the representation of roles in
8:249–261, 1992. object-oriented and conceptual modelling. Data and
Knowledge Engineering, 35:83–106, 2000.
[10] N. Guarino, M. Carrara, and P. Giaretta. An ontology
of meta-level-categories. In Principles of Knowledge [26] R. Studer, V. Benjamins, and D. Fensel. Knowledge
representation and reasoning: Proceedings of the engineering, principles and methods. Data and
fourth international conference (KR94). Morgan Knowledge Engineering, 25(1-2):161–197, 1998.
Kaufmann, 1994.
[27] K. Sycara, M. Klusch, S. Widoff, and J. Lu. Dynamic
[11] N. Guarino and C. Welty. A formal ontology of service matchmaking among agents in open
properties. In R. Dieng, editor, Proceedings of the 12th information systems. ACM SIGMOD Record. Special
EKAW Conference, volume LNAI 1937. Springer Issue on semantic interoperability in global
Verlag, 2000. information systems, 1998.
[28] V. Tamma and T. Bench-Capon. Supporting
[12] N. Guarino and C. Welty. Identity, unity and inheritance mechanisms in ontology representation. In
individuality: Towards a formal toolkit for ontological R. Dieng, editor, Proceedings of the 12th EKAW
analysis. In W. Horn, editor, Proceedings of the 14th Conference, volume LNAI 1937, pages 140–155.
European Conference on Artificial Intelligence Springer Verlag, 2000.
(ECAI), Amsterdam, 2000. IOS Press.
[29] D. Touretzky. The Mayhematics of Inheritance
[13] N. Guarino and C. Welty. Towards a methodology for Systems. Morgan Kaufmann, 1986.
ontology based model engineering. In Proceedings of [30] P. Visser, D. Jones, T. Bench-Capon, and M. Shave.
the ECOOP 2000 workshop on model engineering, Assessing heterogeneity by classifying ontology
2000. mismatches. In N. Guarino, editor, Formal Ontology
in Information Systems. Proceedings FOIS’98, Trento,
[14] N. Jennings. Agent software. In Proc. UNICOM Italy, pages 148–182. IOS Press, 1998.
Seminar on Agent Software, pages 12–27, 1995.
[31] M. Wooldridge and N. Jennings. Intelligent agent:
[15] R. Kowalski and M. Sergot. A logic-based calculus of Theory and practice. Knowledge engineering review,
events. New Generation Computing, 4:67–95, 1986. 1995.