=Paper=
{{Paper
|id=None
|storemode=property
|title=Making Ontology Relationships Explicit in a Ontology Network
|pdfUrl=https://ceur-ws.org/Vol-749/paper14.pdf
|volume=Vol-749
|dblpUrl=https://dblp.org/rec/conf/amw/DiazMR11
}}
==Making Ontology Relationships Explicit in a Ontology Network==
https://ceur-ws.org/Vol-749/paper14.pdf
Making Ontology Relationships Explicit in a
Ontology Network
Alicia Dı́az1 , Regina Motz2 , Edelweis Rohrer2
1
LIFIA, Facultad de Informática,
Universidad Nacional de La Plata, Argentina
alicia.diaz@lifia.info.unlp.edu.ar
2
Instituto de Computación, Facultad de Ingenierı́a,
Universidad de la República, Uruguay
{rmotz, erohrer}@fing.edu.uy
Abstract. The development of ontologies to the Semantic Web is based
on the integration of existing ontologies favoring the modularization and
reuse of them. These ontologies are used together in complex applica-
tions. However, how they are combined is usually hidden in the applica-
tion code. The lack of an approach for explicitly expressing the way how
ontologies are combined for a specific purpose, leads to think on ontol-
ogy networks as a new ontology engineering concept. This paper formally
defines the different relationships among the networked ontologies and
shows how they can be modeled as an ontology network in a case study
of the health domain.
Keywords: ontology network, ontology relationships formaliza-
tion, quality assessment.
1 Introduction
Tom Gruber [1] has defined “ontology” as a “formal, explicit specification of a
shared conceptualization”, what means that ontologies are very useful for struc-
turing and defining the meaning of the metadata terms that are collected inside
a domain community.
Nowadays, autonomously developed ontologies emerge quite naturally in dif-
ferent domains (health, tourism, learning, quality of services, etc.). These on-
tologies, each one built for different purpose, are used together in complex ap-
plications. However, how they are combined is usually hidden in the applica-
tion code, without explicitly expressing the way how ontologies are combined
for a specific purpose. This situation leads to think on ontology networks as a
new ontology engineering concept, which is being increasingly applied, instead of
custom-building new ontologies from scratch. An ontology network differs from a
set of interconnected single ontologies, due to in it the meta-relationships among
the different ontologies involved are explicitly expressed [2]. As part of the NeOn
methodology [3], different scenarios for building ontology networks were identi-
fied, ranging from the construction of an ontology network from scratch up to
the reuse of ontological and non-ontological resources.
2
The main contribution of this paper is a first definition, based on the Descrip-
tion Logics formalism, of the different relationships among networked ontologies.
Our work attempts to assuring the consistency of the ontology network specifi-
cation.
The remainder of this paper is organized as follows. Section 2 gives an
overview of ontology-network and the theoretical background of this work. Sec-
tion 3 introduces the ontology relationship definitions. Section 4 briefly describes
a case study in the health domain, more specifically a recommendation system.
Section 5 details the modelling of an ontology network, applying the specified
ontology relationships among the ontologies of the case study. Finally, Section 6
gives some conclusions and future works.
2 Background
An ontology network can be defined as a collection of ontologies related together
through a variety of different relationships such as mapping, modularization,
and versioning, among others [4]. Distributed and collaborative methodologies
of ontology design, such as DILIGENT [5] and the NeOn project approach [2],
allows the design of local models based on a core model which integrates the
local ones.
Intuitively, defining an ontology network is to select a set of networked on-
tologies, by identifying the different kinds of relationships between the networked
ontologies. There are some models that cover both the syntactic aspects of on-
tology relationships and the semantic aspects of interpreting ontology networks
and their relations. For instance, the Collaborative Ontology Design Ontology
(C-ODO) [6] is an ontology network that describes design entities (ontologies,
modules, activities, etc.). In [4], Allocca et al. present general relations between
ontologies, such as includedIn, equivalentTo, similarTo and versioning, describ-
ing them in the DOOR (Descriptive Ontology of Ontology Relations) ontology.
Grau et al [7] propose a work that adapts important notions of software engi-
neering, such as module and black-box behavior, to be applied in the reuse and
integration of ontologies. In [8], Zimmermann et al. introduce the concept of
Distributed System as a set of ontologies interconnected by ontology alignments,
describing semantic relations between ontologies, such as: cross-ontology concept
subsumption or cross-ontology role subsumption, among others.
There also exist several works that propose formal definitions of the ontology
mapping (or ontology alignment) concept, such as [9–12]. Most of them formalize
the idea of mapping between concepts, relations and instances (so called enti-
ties by [12]) of two ontologies. They also address the problem of finding these
correspondences between entities of different ontologies. But in the real world,
we find other ontology relationships beyond mapping, that is, it is not always
the case that two ontologies are related through an alignment between concepts,
relations or instances. Except for the works [4] and [10], which defines bridge
axioms to express different relationships between ontologies in a general way,
these proposals do not address a formalization which explicitly states the pos-
3
sible different relationships between two ontologies. Our approach is similar to
[4] because it defines ontology relationships. But our proposal differs from [4] in
two main aspects:
– DOOR’s relationships were extended by adding the usesSymbolsOf relation-
ship.
– Our work is an introduction of a formalization of the inter-ontology relation-
ships.
3 Ontology Relationships in a Ontology Network
Based on the NeOn Methodology [13], we have analized how to perform the
combination of different types of knowledge resources for building an ontology
network, formalizing a selected set of ontology relationships. This set of rela-
tionships allowed us to design an ontology network, expressing explicitly the
semantics of the relationships in a particular set of ontologies. This paper is a
first intend of formalization towards the obtaining of a complete and minimal set
of ontology relationships necessary and sufficient to build an ontology network.
We have considered four ontology relationships: isTheSchemaFor, isACon-
servativeExtentionOf and mappingSimilarTo, taken from the DOOR ontology
and one relationship named usesSymbolsOf, identified in the present work. Below
we present a conceptual and a formal definition of each relationship. These def-
initions are based on the Description Logics formalism (DLs) [14–16], applying
the concepts: Signature of a DL [7, 17], concept description, concept definition,
general concept inclusions (GCIs), TBox [14, 16], interpretation [16, 17], ABox
[16], consistency of an ABox w.r.t. a TBox [16], model of an ontology [7] and
logical consequence of an ontology [7].
To introduce the ontology relationship definition we assume we have two on-
tologies O and O0 formalized by a DL L, formally represented by (T , A, I, N)
and (T 0 , A0 , I 0 , N 0 ) respectively, where:
T and T 0 are the TBox,
A and A0 are the ABox consistent w.r.t. T and T 0 , for the models I = (∆I ,.I )
0 0
and I 0 = (∆I ,.I ) respectively,
0
N and N 0 are the sets of individual names of the domains ∆I and ∆I respec-
tively.
isTheSchemaFor relationship
Conceptually, this relationship keeps the link between a model and its meta-
model. A formal definition is:
Let O and O0 be two ontologies.
Let G = {Ci 0 | Ci 0 ∈ T 0 is a concept description}, R = {ri 0 | ri 0 ∈ T 0 is a role
0 I0 I0 0 0 I0 I0 0 0
name}, GI = {Ci 0 | Ci 0 ⊆ ∆I }, RI = {ri 0 | ri 0 ⊆ ∆I × ∆I }
O is the Schema for O 0 (isTheSchemaFor(O, O0 )) if there exists:
4
– a function s : A → G ∪ R that maps each concept assertion C(i) to a concept
description Ci 0 or a role name ri 0 and
0 0
– a function sI : ∆I → GI ∪ RI that maps each domain element iI to the set
0
I I0
Ci 0 or to the binary relation ri 0 , where:
C ∈ T is a concept description, i ∈ N , C(i) ∈ A, iI ∈ C I since A is consis-
tent w.r.t. T for the model I, Ci 0 ∈ T 0 , ri 0 ∈ T 0
isAConservativeExtensionOf relationship
This relation describes an extension of a given ontology by a number of ad-
ditional axioms, which describe what has not been covered yet by the existing
ontology. Formally:
Let O and O0 ⊆ O be two ontologies.
Let sig(O0 ) be the signature of O0 over L;
O is a Conservative Extension of O 0 (isAConservativeExtensionOf(O, O0 ))
w.r.t. L if for every axiom α over L with sig(α) ⊆ sig(O0 ), we have O |= α iff
O0 |= α [7, 17].
mappingSimilarTo relationship
An ontology O isSimilarMappingTo an ontology O0 if there exists an align-
ment from O to O0 and this alignment covers a part of the vocabulary of O. The
formal definition is:
Let O and O0 be two ontologies.
Let K ⊆ {C | C ∈ T is a concept description}, K 0 ⊆ {C 0 | C 0 ∈ T 0 is a concept
I0 I0 0
description}, K I = {C I ⊆ ∆I | C ∈ K}, K 0 = {C 0 ⊆ ∆I | C 0 ∈ K 0 }
O is Mapping Similar to O 0 (isMappingSimilarTo(emphO, O0 )) if there ex-
ists:
– a function m : K → K 0 that maps each concept description C ∈ K to a
concept description C 0 ∈ K 0 and
I0
– K 0 ⊆ K I , that means the subset K I ⊆ ∆I , in the model I for the ontology
I0 0
O, includes the set K 0 ⊆ ∆I of individuals in the model I 0 , for the ABox
A0 in the ontology O0 , that asserts the concepts of the set K 0 which are
mapped to the concepts of the set K by the function m.
This definifion is based on the concepts of morphism [9], ontology alignment
[10], mapping function [11], mapping source and target ontologies [18] and on-
tology alignment function [12].
usesSymbolsOf relationship
The usesSymbolsOf relationship happens when the properties at an ontology
O involves individuals from another ontology O0 , in such a way that the ontol-
ogy O defines some properties that take value in individuals that are classified
by classes of the ontology O0 . The usesSymbolsOf relationship links ontologies
5
O and O0 in such a way that it abstracts from the particular ontology O0 to
be imported and focuses instead on the symbols from O0 that are to be reused.
Previously to introduce the definition of the usesSymbolsOf relationship, it is
necessary to define the safety of an ontology for a signature:
Let O and O0 be two ontologies.
Let S a signature over L.
We say that O is safe for S w.r.t. L, if for every ontology O0 with Sig(O) ∩
Sig(O0 ) ⊆ S, we have that O is a conservative extension of O0 w.r.t. L [7].
Now, we can introduce a formal definition of the usesSymbolsOf relationship:
Let O and O0 be two ontologies with Sig(O) and Sig(O0 ) over L and Sig(O) ∩
Sig(O0 ) ⊆ Sig(O0 ) .
O uses Symbols of O 0 (usesSymbolsOf(emphO, O0 )) if O is safe for Sig(O0 ).
In the next section, we will introduce a case study of a recommender infor-
mation system in the health domain. This case study will help to identify the
different domain ontologies and how they must be combined in order to obtain
the underlying model of this application. For this case study, the set of ontology
relationships previously formalized was enough to explicitly express the links
among the different domain ontologies.
4 A Case Study: Modelling a Health Information
Recommender System
The use of the web as a knowledge repository where common people can find
information, especially in the health area, increases drastically day by day. This
is a very worrying reality because many of health websites do not contain data
of good quality: precise, believable and relevant to user’s profile. In this sense, a
decentralized, intelligent recommender system can automatically give an evalu-
ation about the quality of the sources according to the consumer’s needs. Apart
of the quality data issue, it is necessary to consider other aspects related to the
context in which the user makes the query, like query goals and relevance feed-
back. All these issues lead to shape our health recommender system as based
on quality assurance and context features, to give a reading recommendation of
health resources for a particular user. Our approach involves several knowledge
domains that have to be modeled and integrated as whole. These knowledge
domains are health domain, website domain, quality assurance domain, context
domain and recommendation domain. Below We will describe these knowledge
domains, being each domain independent from each other, favouring the reuse
of models. Particularly, in this paper we conceptualize each domain as an inde-
pendent ontology as it will discuss in Section 5.
The Health Domain refers to terminology about health topics. It models
for example the treatment, risk factors, diagnostic and effects of a disease. These
6
concepts can be refined in terms of a specific disease i.e Alzheimer, and thus can
be modeled the concepts Alzheimer Treatment or Alzheimer Diagnostic.
The WebSite Domain conceptualize the domain of webpages and particu-
larly describe their contents. The main concepts are: web content, web page and
web site, which can be characterized by other concepts such as source and author,
depending on the application environment based on the Web Site domain.
Regarding the Quality Assurance Domain, there exists a scientific ap-
proach that defines data quality dimensions rigorously, as dimensions that can
be intrinsic or not intrinsic to an information system [19]. Some intrinsic data
quality dimensions are: believability, accuracy and objectivity. Eysenbach et al.
[20] present a study of how quality on the Web is evaluated in practice, compar-
ing different methodologies of quality assessment.A domain expert must decide
which dimensions are relevant for a specific domain and must define metrics
in order to measure them. Then, the Quality Assurance Domain conceptualize
metrics, quality assurance specifications and quality assessments. Metrics are
formulas defined based on the properties of resources. For example, a metric can
measure if a web page has an author, or count its number of words, etc. A quality
assurance specification describes the different quality dimensions; for instance,
readability, precision or believability. In order to make a quality assessment, one
or more suitable metrics must be applied. For example a metric to evaluate the
believability dimension of a web resource can be based on the resource’s author.
A quality assessment models the evaluation of a particular web content (i.e. a
web page about Alzheimer) for a particular quality dimension through a specific
metric. It is obtained a quality level, which represents the result of the quality
assessment, for example, ”high” or ”low” believability.
The Context Domain describes user profiles, query situations and user
actions. The user profile features user properties, such as age range or role (for
instance patient or relative). The query situation models the concept of query
goal, that is, explicitly ask users about their goal for each specific query in order
to aid in the recommendation process. This can be done at two levels: type of
query, which represents the possible intentions of a user when makes a query
(informational, navigational or transactional) or topic, which is the issue of the
query, for example Alzheimer. The user action represents the action of the user
when makes a query for a specific task. Once the system had presented the user
with an initial set of documents, the user can usually indicate those documents
that contain useful information, giving his/her relevance feedback, which will be
used as input to produce a reading recommendation of a content.
The Recommendation Domain describes the reading recommendation of
a resource for a user. It models two aspects: the recommendation specification and
the recommendation itself. The former specifies the recommendation definition,
which describes the criterion upon which it relies to make a recommendation
(i.e, a quality dimension), the context aspects and the possible recommenda-
tion levels. The recommendation models concrete recommendation assessments,
based on a recommendation specification.
7
5 Making Explicit Ontology Relationships in a Ontology
Network: a Case Study
In this section we explain how to build an ontology network, applying the on-
tology relationships defined in Section 3, for linking a set of ontologies that
conceptualize the different and independent knowledge domains corresponding
to the recommender system case study described in Section 4. More precisely,
this ontology network is a network of ontology networks; this means that each
knowledge domain involved is itself an ontology network and all of them are re-
lated among each other. Figure 1 outlines this approach; the different ontology
networks are arranged by columns and the relation inter-ontology networks are
shown by arrows crossing columns. In Section 5.1 the ontology relationships will
be used to describe the specific-domain ontology networks and in Section 5.2 the
ontology network is shaped as a network of ontology networks.
Fig. 1. The Health Ontology Network
5.1 Development of the Domain Ontology Networks
This section describes some of the different specific-domain ontology networks,
designed by using the relationships between ontologies introduced above.
Health Domain ontology network comprises the Health and the Specific
Health ontologies. The former conceptualize any diseases, while the last one is
more specific, for instance the Alzheimer ontology.
Both ontologies are related by the isAConservativeExtentionOf relationship.
The Alzheimer ontology isAConservativeExtentionOf the Health ontology, since,
8
for instance, the concept Treatment of the Health ontology is extended by sub-
sumption by the concept AlzheimerTreatment of the Alzheimer. This relationship
is applied because in the health domain, there are some concepts that are gen-
eral for all diseases (Diagnostic, Treatment), which can be reused in the knowl-
edge base for a specific disease, such as alzheimer. Thus, this is the case where
the knowledge expressed in the more generic Health ontology must be entirely
reused in the more specialized Alzheimer ontology. Looking at the definition
of isAConservativeExtentionOf relationship, the Health ontology should not be
compromised by the new axioms. In particular, the extended ontology should
not entail new subsumptions between concepts that are in the original one.
WebSite ontology network is composed by three ontologies: WebSite Spec-
ification, WebSite and WebSite Specialization (Figure 2).
Fig. 2. WebSite Ontology described by the WebSite Specification Ontology
The main concepts of the WebSite Specification ontology are WebResource
and WebResourceProperty. A web resource is any resource which is identified by
a URL; for instance a webpage. Web resource properties models the properties
that can be attached to a web resource, for instance, hasContent, hasSource,
hasAuthor, etc. The WebSite ontology has as main concepts: WebContent, Web-
Page and WebSite, that are more specific. The WebSite Specialization ontology
adds properties to these main concepts, such as hasAuthor and hasSource de-
pending on the application environment.
In this ontology network, the most interesting feature is the use of the is-
TheSchemaFor relationship. The WebSite Specification ontology plays the role
of metamodel for the Website and WebSite Specialization ontologies. Thus, the
concepts and relations of these two ontologies are instances of the concepts We-
bResource and WebResourceProperty in the WebSite Specification ontology. This
matchs the formal definition of the isTheSchemaFor relationship, where a map-
ping is defined between instances that asserts the concepts (ABox) in the meta-
9
model (WebSite Specification ontology) and concepts and relations (TBox) in
the model (WebSite and WebSite Specialization ontologies).
Quality Assurance ontology network is composed by three ontologies:
Metric Specification, Quality Specification and Quality Assessment, as shown in
Figure 3. These ontologies conceptualize metrics, quality assurance specifications
and quality assessments, respectively, as is detailed in Section 4.
Fig. 3. Quality Assurance Ontology Network
As is showed in Figure 1, the Quality Specification ontology is related with
the Metric Specification ontology through the usesSymbolsOf relationship; since
a dimension of quality is always based on a metric. In this case, the occurrence
of the usesSymbolsOf relationship is given by the assessedBy property that as-
sociates to each quality dimension the corresponding metric. It is the case where
although two ontologies (Quality Specification y and Metric Specification) must
be kept separate, there exists one ontology that needs to be linked to one or more
concepts of another ontology, because some properties (in this case: assessedBy
property) of it take values in individuals classified by classes in the used ontol-
ogy (concept Metric of the Metric Specification ontology). If we review the given
definition of the usesSymbolsOf relationship, the Quality Specification ontology
”abstracts” from the particular ontology reused (Metric Specification ontology),
focusing in the symbols to be reused (Metric concept), assuring the safety of the
ontology reused.
Here it is important to clearly establish when to apply the isAConservative-
ExtensionOf relationship and when to apply the usesSymbolsOf relationship.
Reviewing their definitions, the former could be considered as a particular case
of the latter, since in the isAConservativeExtensionOf relationship a ontology
is enterely included in another one, while for the usesSymbolsOf relationship an
ontology includes some elements of another ontology. But there exist a concep-
10
tual difference, which is important to consider when both relationships are used
in a concrete case study. Below we explain the difference.
For the usesSymbolsOf relationship, although the domains of the involved
ontologies are sematically related, the ontologies play different roles (for exam-
ple a ontology models dimensions and another one models metrics). Thus, it can
be inferred that for the usesSymbolsOf relationship the related ontologies are
kept as separate ontologies, despite of one ontology is linked to the other by the
need of reusing some concepts and individuals. However, for isAConservative-
ExtensionOf, the idea is that an ontology extends all the model of the other,
specializing the represented knowledge.
In this work, the Context and Recommendation ontology networks are omit-
ted, they are modeled through the isTheSchemaFor and usesSymbolsOf rela-
tionships.
5.2 The Health Ontology Network as a Network of Ontology
Networks
The above presented ontology networks are also interrelated among each other.
Mainly, they are related by the usesSymbolsOf and mappingSimilarTo relations
(Figure 1).We have just illustrated the usesSymbolsOf relationship in the do-
main ontology networks (Section 5.1). The usesSymbolsOf relationship links a
ontology to individuals of concepts of another ontology, in such a way that al-
though they are separate ontologies, one ontology depends on the other, since
it has properties that involves individuals of it. On the other hand, the map-
pingSimilarTo relationship, keeps the related ontologies even more independent.
As was formalized in the definition, a mapping is defined between concepts of two
ontologies, but none of them depends on the other, sharing a subset of instances
that assert the mapped concepts. This is the reason why mappingSimilarTo is
the relationship that fits in most of the links identified between ontologies from
different domains, as Figure 1 shows.
For instance, the mappingSimilarTo relationship is used between Quality As-
sessment and WebSite ontologies of the Quality Assurance and WebSite domains
respectively. It was defined an alignment between the Resource and WebContent
concepts, in this particular case study of the health domain. But if our case study
were about quality assessment of leaning objects, in the educational domain, we
would define a alignment between the Resource and LearningObject concepts,
and so on, depending on the nature of the individuals to be assessed.
The mappingSimilarTo relationship is also used between Metric Specifica-
tion and WebSite Specification ontologies of the Quality Assurance and Web-
Site domains respectively. It was defined an alignment between the Feature and
WebResourceProperty concepts. Thus, it is possible to specify that a metric is
based on some property of a web resource. Here, it is possible to appreciate the
convenience of having some ontologies that plays the role of metamodel for oth-
ers, because the instances (ABox) of the WebResourceProperty concept of the
WebSite Specification ontology are relations (TBox) of the WebSite ontology
(properties of web contents).
11
The usesSymbolsOf relationship, can be identified (Figure 1) between Web-
Site Specialization and Alzheimer ontologies of the WebSite and Health domains
respectively. For instance, the hasTopic property, from the WebSite Specializa-
tion ontology takes values in the Alzheimer ontology.
6 Conclusions and Future Research Directions
In this paper we have explicitly defined a set of ontology relationships which
allow us to express different links among the ontologies of a ontology network.
We give a formal definition of each relationship, based on the Description Logics
formalism, which prevents from contradictory inferences tailored in an ontology
network.
In addition, we have used the defined relationships to describe an ontology
network that models the different domains related to a health recommender
system. Thus, we have explicited the relationships among the ontologies that
compose each domain ontology network as well as those which link the domain
ontology networks to make up the Health ontology network.
The present work is a first theoretic approach which aims to keep the logical
consistency of the ontology network model and its directions is towards the
obtaining of a complete and minimal set of ontology relationships necessary and
sufficient to build an ontology network.
Starting from the presented design, good practices on Ontology Engineering
lead to evaluate the model in an interaction between ontology engineers and do-
main experts. From this evaluation, it is expected to reach a final refinement of
the structures which compose the ontology network, capitalizing it in method-
ological results.
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