Ontology Change: Classification and Survey

Abstract

Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this paper is to identify the exact relationships between these research areas and to determine the boundaries of each field, by performing a broad review of the relevant literature.

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The Knowledge Engineering Review, Vol. 00:0, 1–29. c
2007, Cambridge University Press
DOI: 10.1017/S000000000000000 Printed in the United Kingdom
Ontology change: classification and survey
GIORGOS FLOURIS1,2, DIMITRIS MANAKANATAS2, HARIDIMOS
KONDYLAKIS2,3, DIMITRIS PLEXOUSAKIS2,3and GRIGORIS ANTONIOU2,3
1Istituto della Scienza e delle Tecnologie della Informazione, C.N.R.
Via Giuseppe Moruzzi, 1, 56124, Pisa, Italy
E-mail: flouris@isti.cnr.it
2Institute of Computer Science, FO.R.T.H.
P.O. Box 1385, GR 71110, Heraklion, Greece
E-mail: fgeo@ics.forth.gr, manakan@ics.forth.gr, kondylak@ics.forth.gr, dp@ics.forth.gr, antoniou@ics.forth.gr
3Computer Science Department, University of Crete
P.O. Box 2208, GR 71409, Heraklion, Greece
E-mail: kondylak@csd.uoc.gr, dp@csd.uoc.gr, ga@csd.uoc.gr
Abstract
Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with
ontologies is the modification of an existing ontology in response to a certain need for change. This
problem is a complex and multifaceted one, because it can take several different forms and includes several
related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it
is being addressed by several different, but closely related and often overlapping research disciplines.
Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with
different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this
paper is to identify the exact relationships between these research areas and to determine the boundaries
of each field, by performing a broad review of the relevant literature.
1 Introduction
Originally introduced by Aristotle, ontologies are formal models about how we perceive a domain of
interest and provide a precise, logical account of the intended meaning of terms, data structures and other
elements modeling the real world. As such, they are often viewed as the key means through which the
Semantic Web vision (Berners-Lee et al. 2001) can be realized and have already found several applications
in the area of Knowledge Representation (KR) and in the Semantic Web. Ontologies are so important in
the Semantic Web because they provide a means to formally define the basic terms and relations that
comprise the vocabulary of a certain domain of interest (Lambrix & Edberg 2003), enabling machines to
process information provided by human agents. As a result, ontologies can help in the representation of
the content of a web page in a formal manner, so as to be suitable for use by an automated computer agent,
crawler, search engine or other web service. The importance of ontologies in current Artificial Intelligence
(AI) research is also emphasized by the interest shown by both the research and the enterprise community
to various problems related to ontologies and ontology manipulation (McGuiness et al. 2000).
Ontologies are often large and complex structures, whose development and maintenance give rise to
several interesting research problems. One of the most important such problems is ontology change, which
refers to the generic problem of changing an ontology in response to a certain need; in this paper, the term
will be used in a broad sense, covering any type of change, including changes to the ontology in response
to external events, changes dictated by the ontology engineer, changes forced by the need to translate the
ontology in a different language or terminology and so on. The importance of this problem is emphasized
by recent studies, which suggest that change, rather than ontologies, should be the central concept of the
Semantic Web (Tzitzikas & Kotzinos 2007).

2G.FLOURIS,D.MANA KA NATAS et al.
In order to cope with the many different aspects of the problem of ontology change, several related
research disciplines have emerged (such as ontology evolution, versioning, merging, mapping, matching
etc), each dealing with a different facet of the problem. These areas are greatly interlinked, and have
common elements and subtasks (Noy & Musen 2004). As a result, several research efforts and systems
deal with more than one of these topics creating significant confusion to a newcomer (see table 2 in the
appendix). This confusion is further increased by the fact that certain terms are often used (some would
say abused) with different meanings in the relevant literature, denoting similar, but not identical, research
directions or concepts. For examples of such confusing and overused terms refer to (Flouris & Plexousakis
2005), (Pinto et al. 1999).
We believe that this lack of a standard terminology constitutes a major bottleneck for the ontology
change community, causing an unnecessary confusion as well as misunderstandings. The purpose of this
work is the introduction of a terminology, following the most common uses of the various terms in the
literature. Fixing this terminology will allow us to determine the boundaries of each field as well as to get
a grip on their interactions and connections.
To do that, we perform a literature review on the field of ontology change and introduce a broadly
accepted terminology that will, hopefully, serve as a point of reference for the ontology change
community. The focus of this review is on classification and breadth of coverage, rather than on depth
of analysis; our purpose is to give a clear overall picture of each relevant subfield and determine the
boundaries, interactions and overlaps between the various research areas. The interested reader is referred
to the numerous bibliographic references that will appear throughout this paper for more details on each
area or deeper results; a more compact reference guide is table 2 in the appendix, where a summary of the
referenced works and their relation to the various ontology change subfields can be found.
2 Ontologies and Ontology Change
2.1 What is an Ontology?
The term ontology has come to refer to a wide range of formal representations, including taxonomies,
hierarchical terminology vocabularies or detailed logical theories describing a domain (Noy & Klein
2004). For this reason, a precise definition of the term is rather difficult and different definitions have
appeared in the literature (see, for example, (Gruber 1993a), (Guarino 1998)). One commonly used
definition is based on the original use of the term in philosophy, where an ontology is a systematic account
of Existence. For AI systems, what “exists” is that which can be represented (Gruber 1993b); therefore,
an ontology in the AI context is a structure that specifies a conceptualization, or, more accurately, a
specification of a shared conceptualization of a domain (Gruber 1993a).
A more formal, algebraic, approach, identifies an ontology as a pair hS, Ai, where Sis the vocabulary
(or signature) of the ontology (being modeled by some mathematical structure, such as a poset, a lattice
or an unstructured set) and Ais the set of ontological axioms, which specify the intended interpretation of
the vocabulary in a given domain of discourse (Kalfoglou & Schorlemmer 2003). A similar definition is
given in (De Bruijn et al. 2004), where the signature Sis broken down in three (not necessarily disjoint)
sets, the set of concepts (C), the set of relations (R) and the set of instances (I); thus, an ontology is
defined as a 4-tuple hC, R, I, Ai. Here, we will use the simpler hS, Aidefinition, i.e., we will use Sto
denote the signature of the ontology and Ato denote its axiomatic part.
2.2 Ontology Change
As already mentioned, ontology change refers to the generic process of changing an ontology in response
to a certain need. Several reasons for changing an ontology have been identified in the literature. An
ontology, just like any structure holding information regarding a domain of interest, may need to change
simply because the domain of interest has changed (Stojanovic et al. 2003); but even if we assume a static
world (domain), which is a rather unrealistic assumption for most applications, we may need to change
the perspective under which the domain is viewed (Noy & Klein 2004), or we may discover a design
flaw in the original conceptualization of the domain (Plessers & de Troyer 2005); we may also wish to

Ontology change: classification and survey 3
incorporate additional functionality, according to a change in users’ needs (Haase & Stojanovic 2005).
Similarly, a contradiction (usually an inconsistency or incoherency, see (Flouris et al. 2006a)) may be
spotted, in which case we may want to take some action against the contradiction.
Furthermore, new information, which was previously unknown, classified or otherwise unavailable
may become available or different features of the domain may become known and/or important (Heflin et
al. 1999). Moreover, ontology development is becoming more and more a collaborative and parallelized
process, whose subproducts (parts of the ontology) need to be combined to produce the final ontology
(Klein & Noy 2003), (Noy et al. 2006); this process would require changes in each subontology to
reach a consistent and valid final state; but even then, the so-called final state is rarely final, as ontology
development is usually an ongoing process (Heflin et al. 1999).
There are also reasons related to the distributed nature of the Semantic Web: ontologies are usually
depending on other ontologies, over which the knowledge engineer may have no control; if the remote
ontology is changed for any of the above reasons, the dependent ontology might also need to be modified
to reflect possible changes in terminology or representation (Heflin et al. 1999). In other cases, a certain
agent, service or application may need to use an ontology whose terminology or representation is different
from the one it can understand (Euzenat et al. 2004); in such cases, some kind of translation (change)
needs to be performed in the imported ontology to be of use. Last but not least, we may need to combine
information from two or more ontologies in order to produce a more appropriate one for a certain
application (Pinto et al. 1999).
The problem of ontology change is far from trivial. Several philosophical issues related to the general
problem of adaptation of knowledge to new information have been identified in the research area of
belief change, also known as belief revision (Alchourron et al. 1985), (G¨
ardenfors 1992a), (G¨
ardenfors
1992b), (Hansson 1994), (Katsuno & Mendelzon 1990); most of them are also applicable to knowledge
represented in ontologies (Flouris & Plexousakis 2005), (Flouris & Plexousakis 2006). The large size of
modern day ontologies complicates this problem even further (McGuiness et al. 2000). But it’s not just
that: the Semantic Web is characterized by decentralization, heterogeneity and lack of central control or
authority. This is both a blessing and a curse; these features have greatly contributed to the success of
the WWW (and constitute key features of the Semantic Web) but they have also introduced several new,
challenging and interesting problems, which don’t exist in traditional AI.
As far as ontology change is concerned, one such problem is the lack of control over who uses a certain
ontology once it has been published. Subtle changes in an ontology may have unforeseeable effects in
dependent applications, services, data and ontologies (Stojanovic et al. 2002); ontology designers cannot
know who uses which part of their ontology and for what purpose, so they cannot predict the effects that
a given change on their ontology would have upon dependent elements. The same holds in the opposite
direction: if an ontology is depending on other ontologies, there is no way for the ontology designer to
control when and how these ontologies will change. These facts raise the need to support and maintain
different interoperable versions of the same ontology (Heflin et al. 1999), (Huang & Stuckenschmidt
2005), (Klein et al. 2002), a problem greatly interwoven with ontology change (Klein & Fensel 2001). On
the other hand, heterogeneity leads to the absence of a standard terminology for any given domain which
may cause problems when an agent, service or application uses information from two different ontologies
(Euzenat et al. 2004). As ontologies often cover overlapping domains from different viewpoints and with
different terminology, some kind of translation may be necessary in many practical applications.
All these arguments indicate the importance of the problem of ontology change and motivate us
to use the term in order to cover all aspects of ontology dynamics, as well as the problems that are
indirectly related to the change operation such as the maintenance of different versions of an ontology or
the translation of ontological information in a common terminology. More specifically, we will use the
term ontology change to refer to the problem of deciding the modifications to perform upon an ontology
in response to a certain need for change as well as the implementation of these modifications and the
management of their effects in depending data, services, applications, agents or other elements.
Notice that the decision on the modifications to perform may be made automatically, semi-
automatically or manually; the implementation of the chosen modifications may (but need not) involve

4G.FLOURIS,D.MANA KA NATAS et al.
keeping a copy of the original ontology (versioning). The need to change the ontology may take several
different forms, including, but not limited to, the discovery of new information (which could be some
instance data, another ontology, a new observation or other), a change in the focus or the viewpoint
of the conceptualization, information received by some external source, a change in the domain (i.e., a
dynamic change in the modeled world), the discovery of a problematic pattern in the modeling process,
communication needs between heterogeneous sources of information, the fusion of information from
different ontologies and so on.
2.3 Ontology Change Subfields: A Short Discussion
Our definition of ontology change covers several related research fields which are studied separately in
the literature. These fields are greatly interlinked and several papers and systems deal with more than one
of these problems (see table 2 in the appendix). In other cases, the same term is used in different papers
to describe different research areas. This situation can easily lead to misunderstandings, confusion and
unnecessary waste of effort, especially for a newcomer. In the remainder of this paper we will attempt
to precisely define the boundaries of each ontology change subarea and uncover their relations and
differences. This attempt will hopefully draw a fine line between these areas, allowing the clarification
of the meaning of each term and making the differences and similarities between them explicit. The
provided definitions will not be arbitrary, but will be based on the most common uses of each term in the
literature and on similar previous attempts, like (Kalfoglou & Schorlemmer 2003), (Pinto et al. 1999).
In particular, we will identify and study 10 subfields of ontology change, namely ontology mapping,
morphism, matching, articulation, translation, evolution, debugging, versioning, integration and merging;
in addition, we will clarify the meaning of the term ontology alignment, which is closely related to
ontology matching. Each of these areas deals with a certain facet of the problem of change from a different
view or perspective, covering different application needs, change scenarios or “needs for change”. In this
subsection, we provide a very short description of each of these fields; for more details, the reader is
referred to the following sections, where the properties of each field are discussed in detail.
The first five fields in the above list (ontology mapping, morphism, matching, articulation and
translation), as well as ontology alignment, are studied in section 3 and deal with heterogeneity resolution,
i.e., how to resolve differences in terminology, language or syntax between ontologies. Usually, this
problem is solved by providing a set of “translation rules” that identify similar ontology elements. The
distinguishing difference between these fields is the methodology followed and the expected type of output
(translation rules). These fields may look unrelated to ontology change, as there is no obvious “change”
performed in the involved ontologies; translation rules do not seem to constitute change themselves.
However, heterogeneity resolution falls under the definition of ontology change, in the wide sense of
the term that we use in this paper.
Indeed, consider two agents with heterogeneous ontologies that need to communicate and a set of
translation rules that allows this communication. In this particular example, the driving force (need) behind
the process is the need for communication. The translation rules produced do not directly modify any
ontology; however, they allow each agent to change the other agent’s ontology locally to fit his own
terminology, language and syntax. So the change in this case is made on-the-fly by each agent during each
message exchange and it is trivial, given the translation rules. In this sense, heterogeneity resolution can
be considered a type of ontology change that provides us with a method to change an ontology (but does
not perform the change directly).
Furthermore, it is important to note that heterogeneity resolution constitutes a prerequisite for any
type of successful ontology change, as it makes no sense to try to change an ontology in response to new
information unless both the ontology and the new information are formulated using the same terminology,
language and syntax. So, it makes practical sense to study these fields along with the problem of ontology
change; this is also apparent in the relevant literature (see table 2 in the appendix), where many research
efforts, systems or algorithms that deal with some specific aspect (subfield) of ontology change also deal
with the problem of heterogeneity resolution (e.g., (De Bruijn et al. 2004), (Chalupsky 2000), (McGuiness
et al. 2000), (Noy & Musen 1999a), (Noy & Musen 1999b), (Noy & Musen 2000)).

Ontology change: classification and survey 5
Table 1 Summary of the various subfields of ontology change
Ontology Mapping Purpose:
Input:
Output:
Properties:
Heterogeneity resolution, interoperability of ontologies
Two (heterogeneous) ontologies
A mapping between the ontologies’ vocabularies
The output identifies related vocabulary entities
Ontology Morphism Purpose:
Input:
Output:
Properties:
Heterogeneity resolution, interoperability of ontologies
Two (heterogeneous) ontologies
Mappings between the ontologies’ vocabularies and axioms
The output identifies related vocabulary entities and axioms
Ontology Matching
(its output is called
Ontology Alignment)
Purpose:
Input:
Output:
Properties:
Heterogeneity resolution, interoperability of ontologies
Two (heterogeneous) ontologies
A relation between the ontologies’ vocabularies
The output identifies related vocabulary entities
Ontology Articulation Purpose:
Input:
Output:
Properties:
Heterogeneity resolution, interoperability of ontologies
Two (heterogeneous) ontologies
An intermediate ontology and mappings between the vocabular-
ies of the intermediate ontology and each source
The output is equivalent to a relation and identifies related
vocabulary entities (like ontology matching)
Ontology Translation
(first reading)
Purpose:
Input:
Output:
Properties:
Translation to a different ontology representation language
An ontology and a target ontology representation language
An ontology expressed in the target language
Should produce an equivalent ontology, if possible
Ontology Translation
(second reading)
Purpose:
Input:
Output:
Properties:
Implementation of a vocabulary mapping
An ontology and a mapping
An ontology
Implements a vocabulary change to the source ontology as
specified by the input mapping
Ontology Evolution Purpose:
Input:
Output:
Properties:
Respond to a change in the domain or its conceptualization
An ontology and a (set of) change operation(s)
An ontology
Implements a (set of) change(s) to the source ontology
Ontology Debugging
(is split into
Ontology Diagnosis
and Ontology Repair)
Purpose:
Input:
Output:
Properties:
Restore an ontology’s consistency or coherency
An inconsistent/incoherent ontology
A consistent/coherent ontology
Renders an ontology consistent/coherent
Ontology Versioning Purpose:
Input:
Output:
Properties:
Transparent access to different versions of an ontology
Different versions of an ontology
A versioning system
Uses version ids to identify versions; provides transparent access
to the correct version; determines compatibility
Ontology Integration Purpose:
Input:
Output:
Properties:
Fuse knowledge from ontologies covering similar domains
Two ontologies (covering similar domains)
An ontology
Fuses knowledge to cover a broader domain
Ontology Merging Purpose:
Input:
Output:
Properties:
Fuse knowledge from ontologies covering identical domains
Two ontologies (covering identical domains)
An ontology
Fuses knowledge to describe the domain more accurately
The problems of ontology evolution and ontology debugging are very similar in nature and are studied
in section 4. Ontology evolution deals with the problem of incorporating new information in an existing
ontology, so it deals with the changes themselves. As the new information may contradict the existing one,
part of the problem is how to guarantee that the change will not cause any contradictions in the resulting
ontology. This is closely related to the problem of ontology debugging, whose purpose is to restore the
consistency (or coherency) of an inconsistent (or incoherent) ontology.
Ontology versioning manages different versions of a changing ontology, trying to minimize any
adverse effects that a change could have upon related (dependent) ontologies, agents, applications or

6G.FLOURIS,D.MANA KA NATAS et al.
other elements. This is done by providing transparent access to either the current or some older version of
the ontology, depending on the accessing element. This ability allows the accessing (dependent) elements,
to upgrade to the new version at their own pace (if at all), which is considered a very useful feature, given
the distributed and decentralized nature of the Semantic Web (Heflin et al. 1999), (Heflin & Pan 2004).
Unfortunately, the goals of ontology evolution and versioning are often confused in the literature, so we
chose to study them together (in section 4).
Ontology integration and merging both deal with the fusion of knowledge from two or more source
ontologies. There is a subtle difference between them, related to the domain covered by the source
ontologies (Pinto et al. 1999). In section 5, we provide further details on these two areas and clarify
their differences.
Figure 1 A classification of ontology change subfields
In figure 1 the various fields related to ontology change are classified according to the need for change
that motivates them; we can identify four groups, one related to heterogeneity resolution, one related to
the modification of ontologies, one related to the combination of information from different ontologies
and one related to versioning. Each group contains one or more of the ontology change subfields that

Ontology change: classification and survey 7
are studied in this paper. Similarly, table 1 provides another compact description of the studied ontology
change subfields and will be referenced throughout this work: in table 1, we summarize each subfield
separately by describing the need for change that motivates each field (purpose), the expected input of an
algorithm that deals with the problem (input), its expected output (output), as well as certain comments
on its desired properties (properties).
3 Heterogeneity Resolution
3.1 Discussion on Heterogeneity Resolution
Works related to these areas try to mitigate the problems caused by the heterogeneity of the Semantic
Web. The general motivation for these research efforts is that different ontologies (and sources of
information based upon different ontologies) generally use different terminology, different representation
languages and different syntax to refer to the same or similar concepts. A nice list of scenarios where this
heterogeneity may cause problems can be found in (Euzenat et al. 2004).
The solution that is generally proposed for the problem stated above consists of a set of translation
rules of some kind that allow us to nullify the differences in terminology or syntax. To put it simply, the
goal of the whole process is to make two ontologies refer to the same entities using the same name and
to different entities using different names. For example, an ontology matching algorithm should be able
to identify that two concepts (classes) RESEARCHER and RESEARCH STAFF MEMBER that appear
in two different ontologies refer to the same real-world concept, i.e., the class of all researchers. It should
also be able to differentiate between two different uses of the entity CHAIR, as it could refer to the class
of chairs (as a furniture) in one ontology and to the people forming a Workshop’s Chair in another.
Therefore, these areas basically deal with the same problem (i.e., how to deal with heterogeneous
information); however, the output of the methods (i.e., the result that is produced towards the solution of
the problem) is different in each field. Thus, the different research areas can be identified based on the
type of translation rules that is produced as the output. Due to the close relationship between these topics,
sometimes the terms ontology alignment (in (Euzenat et al. 2004)), ontology mapping (in (Kalfoglou
& Schorlemmer 2003)), or ontology-schema matching (in (Shvaiko & Euzenat 2005)) are used to refer
collectively to all these areas. In the following, we will provide a definition of these fields and outline their
differences and similarities.
3.2 Ontology Mapping, Morphism, Matching, Alignment, Articulation and Translation
The term ontology mapping refers to the task of relating the vocabulary of two ontologies that share
the same domain of discourse in such a way that the mathematical structure of ontological signatures
and their intended interpretations, as specified by the ontological axioms, are respected (Kalfoglou &
Schorlemmer 2003). A similar (and equivalent) definition appears in (De Bruijn et al. 2004) where
ontology mapping is defined as a (declarative) specification of the semantic overlap between two
ontologies, which can be either one-way (injective) or two-way (bijective). The result of an ontology
mapping algorithm is a function (morphism) between ontological signatures. We could differentiate
between two different types of mappings, namely the total ontology mappings and the partial ontology
mappings. A total ontology mapping maps the whole source ontology (say O1=hS1, A1i) to the target
ontology O2while a partial ontology mapping maps a subontology of O1(say O10=hS10, A10i, with
S10 ⊆ S1and A10 ⊆ A1) to O2(Kalfoglou & Schorlemmer 2003).
This definition restricts the mappings to ontological signatures. A more ambitious and interesting
approach would be to create mappings that deal with both the signatures and the axioms of the ontologies.
The term ontology morphism refers to that approach, i.e., the development of a collection of functions
(morphisms) that relate both ontological signatures and axioms (Kalfoglou & Schorlemmer 2003). Notice
that ontology morphism, unlike the other fields discussed in this section, is not restricted to the ontology
vocabulary only, but covers the ontological axioms as well.
In both ontology mapping and ontology morphism, we try to relate the two ontologies via functions.
Alternatively, the two ontologies could be related in a more general fashion, namely by means of a relation.

8G.FLOURIS,D.MANA KA NATAS et al.
The task of finding relationships between (vocabulary) entities belonging to two different ontologies is
called ontology matching and the output of that task is called ontology alignment (Shvaiko et al. 2006).
Equivalently, we could say that ontology matching is the task of creating links between two ontologies
(Choi et al. 2006). Another similar (but less specific) definition appears in (De Bruijn et al. 2004), where
ontology matching is defined as the process of discovering similarities between two source ontologies.
The output of ontology matching (i.e., ontology alignment) is a binary relationship between the
vocabularies of the two ontologies. This approach is more liberal, allowing greater flexibility, so it is more
commonly used in practice. In ontology matching, the sources become consistent with each other but are
kept separate, so ontology matching is usually performed when dealing with complementary domains.
Notice that, in several cases, e.g., (Aumueller et al. 2005), (David et al. 2006), (Giunchiglia et al. 2006),
(Hu & Qu 2006), (Shvaiko & Euzenat 2005), (Zhdanova & Shvaiko 2006), the term ontology (schema)
matching is used to refer to ontology alignment and the two terms are used interchangeably (see also
(Madhavan et al. 2005)).
A binary relationship could be decomposed into a pair of total functions from a common intermediate
source; therefore, the alignment of two ontologies could be described by means of a pair of ontology
mappings from a common intermediate ontology. We use the term ontology articulation to refer to
the process of determining the intermediate ontology and the two mappings to the initial ontologies
(Kalfoglou & Schorlemmer 2003).
Finally, the term ontology translation is used in the literature to describe two different things. Under one
understanding (Kalfoglou & Schorlemmer 2003), ontology translation refers to the process of changing
the formal representation of the ontology from one language to another (say from OWL (Dean et al. 2004)
to RDF (Miller et al. 2006)). This changes the syntactic form of the axioms, but not the vocabulary or the
semantics of the ontology. Under the second understanding (Kalfoglou & Schorlemmer 2003), ontology
translation refers to a translation of the vocabulary, in a manner similar to that of ontology mapping. The
difference between ontology mapping and ontology translation is that the former specifies the function
that relates the two ontologies’ vocabularies, while the latter applies this function to actually implement
the mapping.
3.3 Matchers and Heterogeneity Resolution
In the previous subsection it was made clear that the aforementioned research areas try (with a few excep-
tions) to relate the vocabularies of two ontologies. This problem is far from trivial; an implementation of
a matching-mapping process may use multiple match algorithms or matchers. This allows the selection of
the matchers depending on the application domain and schema types.
Given that the use of multiple matchers may be required, two subproblems emerge. The first is the
design of individual matchers, each of which computes a mapping (or relationship) based on a single
matching criterion. The second is the combination of individual matchers, either by using multiple
matching criteria within a hybrid matcher (e.g., name and type equality) or by combining multiple match
results produced by different match algorithms in parallel or sequentially. The matching approaches can
also be discriminated on the basis of the kind of relations that they produce. Some consider symmetric
(equivalence) relations, while others additionally use asymmetric relations such as subsumption or
implication. A detailed classification appears in figure 2 (Manakanatas & Plexousakis 2006), which is
an extended and improved version of a figure appearing in (Rahm & Bernstein 2001). For individual
matchers, the following largely-orthogonal criteria are considered for classification (a more detailed
description of these methods can be found in (Euzenat et al. 2004)):
•Instance vs. schema: Matching approaches can consider instance data i.e., data content (as in the
system GLUE (Doan et al. 2000), (Doan et al. 2002)), or only schema-level information (as in
Cupid (Madhavan et al. 2001), SF (Melnik et al. 2002), COMA++ (Aumueller et al. 2005) and its
predecessor, COMA (Do & Rahm 2002)).
•Element vs. structure matching (or extensional vs intensional respectively):Match can be performed
for individual ontology elements, such as attributes (as in GLUE (Doan et al. 2002) and COMA,

Ontology change: classification and survey 9
COMA++ (Aumueller et al. 2005), (Do & Rahm 2002)), or for combinations of elements, such as
complex schema structures (as in the systems SF (Melnik et al. 2002), Protoplasm (Bernstein et al.
2004) and S-MATCH (Giunchiglia et al. 2004)).
•Language vs. constraint: A matcher can use a linguistic approach (e.g., based on names and textual
descriptions of ontology elements, like in (Manakanatas & Plexousakis 2006), (Ferrara 2004)), or a
constraint-based approach (e.g., based on keys and relationships, like in S-MATCH (Giunchiglia et
al. 2004) and GLUE (Doan et al. 2002)).
•Matching cardinality: Each element of the resulting matching may match one or more elements of
one schema to one or more elements of the other, yielding four cases: 1:1, 1:n, n:1, n:m. In addition,
there may be different matching cardinalities at the instance level. This criterion determines whether
we have a matching or a mapping algorithm.
•Auxiliary information: Most matchers not only rely on the input ontologies, but also on auxiliary
information, such as dictionaries (e.g., in (Manakanatas & Plexousakis 2006), (Ferrara 2004)), global
schemas (e.g., in Clio (IBM corporation)), previous matching decisions (e.g., in COMA, COMA++
(Aumueller et al. 2005), (Do & Rahm 2002) and in (Manakanatas & Plexousakis 2006)), or user input
(e.g., in Clio (IBM corporation) and Cupid (Madhavan et al. 2001)).
Figure 2 Classification of matching approaches

10 G.FLOURIS,D.MANA KA NATAS et al.
3.4 State of the Art in Heterogeneity Resolution
In most cases, the process of heterogeneity resolution determines a similarity value in the interval [0,1]
for every possible matching between two ontological elements. PROMPT (Noy & Musen 2000), (Noy
& Musen 1999a) (originally called SMART (Noy & Musen 1999b)) and Chimaera (McGuiness et al.
2000) are two interesting systems dealing with heterogeneity. Another important system is Clio (IBM
corporation): Clio consists of a set of Schema Readers, which read a schema and translate it to an internal
representation, a Correspondence Engine, which is used to identify matching parts of ontologies, and a
Mapping Generator, which generates view definitions to map data from the source ontology into data in
the target ontology. The Correspondence Engine uses n:m element level matchings (i.e., relationships)
that have been gained by knowledge or provided by the user via a graphical interface.
On the contrary, Cupid (Madhavan et al. 2001) is a hybrid schema matcher that combines a name
matcher and a structure-based matcher. This tool finds the element matchings of a schema, using the
similarity of their names and types at the leaf level. The system SF (Melnik et al. 2002) uses certain filters
that allow the user to choose the best matchings from a set of possible ones returned from the structure-
based matcher; this system uses no external dictionary. A general-purpose approach to the problem of
translation is described in (Chalupsky 2000). In (Castano et al. 2006b), an ontology matching algorithm
(namely H-MATCH (Castano et al. 2006a)) is used as an aid to the ontology evolution process.
In (Calvanese et al. 2002), an interesting formal framework for defining mappings is proposed;
however, this work is placed in the context of ontology integration and the focus is on being able to answer
queries over heterogeneous ontologies using the defined mappings. (Mocan et al. 2006) use the notion
of perspective to define another formal framework for mapping creation; this model is applied in order
to visualize the results of the mapping process. Similarly, (Kondylakis et al. 2006) present a language
used to describe ontology alignments, which is adequate to capture the most common occurrences of
heterogeneity encountered in a broad sample of cases, yet simple enough to be used by domain experts.
In (Manakanatas & Plexousakis 2006) the term ontology matching is used to refer to an ontology
mapping algorithm based on the linguistic properties of terms, using a thesaurus based on WordNet
(Miller 1995); a similar approach appears in (Ferrara 2004). In (Stoilos et al. 2005), a certain string metric
is proposed to evaluate name similarities of elements in different ontologies, upon which a linguistic
matcher could be based. Some thoughts on the issue of heterogeneity in the context of the SHOE
language can be found in (Heflin et al. 1999), (Heflin & Hendler 2000). A machine learning approach for
ontology matching, which is based on shared individuals (instance-based matcher), appears in (Palmisano
et al. 2006). An interesting method of improving the results of a matching process, which exploits user
validation combined with machine learning techniques, can be found in (Ehrig et al. 2005). In (Mitra et
al. 2005), a probabilistic technique is used towards this aim; in that work, the final similarity evaluation of
an ontology mapping algorithm is affected by the similarity probabilities of each entity’s neighborhood,
improving the initial mapping result. Another method based on probabilistic analysis, which takes into
account uncertainty issues in the mapped ontologies can be found in (Pan et al. 2005).
Protoplasm (Bernstein et al. 2004) is an interesting system that offers a new architecture for schema
matching; it includes a special internal representation, called the Schema Matching Model Graph, as
well as an interface which handles the mapping algorithms and a smart technique for combining their
execution. COMA++ (Aumueller et al. 2005) offers a large library of matchers and supports several ways
of combining their results. These matchers support finding structure-based matchings as well as element-
level matchings.
Most of the aforementioned approaches are intensional (schema-based) and symmetric; none is both
asymmetric and extensional (element-based). A symmetric and extensional approach is GLUE (Doan
et al. 2000), (Doan et al. 2002), which uses Bayesian learners in order to classify instances of the one
ontology into the other and vice-versa; this allows the estimation of the joint probability distribution and
the prediction of concept similarities. To the best of our knowledge, there is only one intensional method
considering asymmetric relations, namely S-MATCH (Giunchiglia et al. 2004); S-MATCH identifies
equivalence, more general, less general, mismatch and overlapping relations between concepts and uses a
lot of single matchers: 13 linguistic-based and 3 logic-based ones.

Ontology change: classification and survey 11
Currently, there are more than 50 systems available dealing with ontology matching (Shvaiko et al.
2006) and a lot of research effort is being put in the area. The interested reader is referred to (De
Bruijn et al. 2004), (Choi et al. 2006), (Euzenat et al. 2004), (Euzenat & Shvaiko, 2007), (Kalfoglou
& Schorlemmer 2003), where a more extensive list of systems and works related to these research areas
can be found. Evaluations of such works appear in (Avesani et al. 2005), (Do et al. 2002), (Svab et al.
2007), (Yatskevich 2003).
Unfortunately, heterogeneity resolution in ontologies is a time consuming process and relies heavily
on human intervention. Several works envisage the process becoming fully automatic so that it becomes
more practical (Kalfoglou & Schorlemmer 2003). In this direction, advances in the field of natural
language processing will probably help researchers improve their understanding on the processes behind
automatic heterogeneity resolution (Kalfoglou & Schorlemmer 2003). We argue however that the problem
of heterogeneity resolution cannot be dealt with using solely fully automatic algorithms; the most realistic
path is the development of semi-automatic solutions that would reduce human intervention.
4 Ontology Evolution, Debugging and Versioning
4.1 Disambiguating the Terms
In some works, ontology versioning is considered a stronger variant of ontology evolution (Haase
& Sure 2004). Under that viewpoint, ontology evolution is concerned with the ability to change the
ontology without losing data or negating the validity of the ontology, whereas ontology versioning
should additionally allow access to different variants of the ontology. Thus, while ontology evolution is
concerned with the validity of the newest version, ontology versioning additionally deals with the validity,
interoperability and management of all previous versions, including the current (newest) one.
This viewpoint is influenced by related research on relational and object-oriented database schema
evolution and versioning (Banerjee et al. 1987), (Franconi et al. 2000), (Kim & Chou 1988), (Peters &
Ozsu 1997). A recent survey on this issue, studying the differences and similarities of the two contexts can
be found in (Noy & Klein 2004), where ontologies are compared with database schemas outlining their
differences and these differences’ impact with respect to (ontology and database schema) evolution and
versioning. In the same paper it is argued that, under the understanding of (Haase & Sure 2004), ontology
evolution and versioning become indistinguishable; due to the distributed and decentralized nature of the
Semantic Web, multiple versions of ontologies are bound to exist and must be supported. Furthermore,
ontologies and dependent elements are likely to be owned by different parties or spread across different
servers; as a result, some parties may be unprepared to change and others may even be opposed to it
(Heflin et al. 1999). All these facts force us to maintain and support different versions of ontologies,
making ontology evolution (under this understanding) useless in practice.
From a similar standpoint, the evolution of an ontology represents the evolution of our understanding
of the domain. Thus, a newer version of an ontology does not invalidate the old one, but replaces it in
terms of usability (only); as a result, the different versions of an evolving ontology should coexist. This
is reminiscent of the viewpoint employed in temporal databases (Tansel et al. 1993). The adoption of this
viewpoint makes ontology versioning the only valid method of changing an ontology, whereas evolution
is an internal part of versioning, dealing only with the determination of the next version.
We believe that the issue of modifying the ontology (ontology evolution) should be clearly separated
from the issue of maintaining the interoperability of the different versions of the ontology that occur
because of these modifications (ontology versioning). This distinction is not always clear in the literature,
because the extensive web of interrelationships that is usually formed around an ontology forces us to
consider the issue of propagating the changes to dependent elements as an indispensable part of the
changes themselves (Maedche et al. 2003). This tight coupling has caused ontology evolution algorithms
(and systems) to deal with versioning issues as well.
For example, according to (Plessers et al. 2005), the purpose of ontology evolution is to define methods
(algorithms) to cope with ontology changes and techniques to maintain consistency of depending artifacts.
Similarly, in (Stojanovic et al. 2002), ontology evolution is defined as the timely adaptation of an ontology

12 G.FLOURIS,D.MANA KA NATAS et al.
to changed business requirements, to trends in ontology instances and patterns of usage of the ontology-
based application, as well as the consistent management and propagation of these changes to dependent
elements. Some would consider that these definitions include both ontology evolution and versioning.
What is important here is to clarify the details of the propagation process and whether the original
version of the ontology will still be available after the change has been completed (and propagated).
If, after informing dependent ontologies and other elements of the changes that were just performed in
the local ontology (propagation of changes), the original version ceases to be available, then the role of
propagation is to let these elements know that future interaction might be problematic unless the dependent
elements synchronize themselves with the change. In this case, the process of propagation can be rightfully
considered a part of the change itself, i.e., a part of ontology evolution.
On the contrary, if the propagation has a purely informative character and both the new and the old
versions are available, then the dependent elements can synchronize with the changes at their own pace
(if at all); to support this desirable feature (Heflin et al. 1999), the local ontology needs to employ an
ontology versioning algorithm which will be responsible for managing and providing proper access to the
old and the new version. This algorithm cannot be considered part of the ontology evolution process.
Despite the obvious relationships between ontology evolution and versioning algorithms, caused by
ontology interoperability, we firmly believe that they are independent research areas facing different
research challenges; thus, our definitions will strictly separate the two areas (see also table 1). More
specifically, we define ontology evolution to refer to the process of modifying an ontology in response
to a certain change in the domain or its conceptualization (Flouris & Plexousakis 2005). On the other
hand, ontology versioning refers to the ability to handle an evolving ontology by creating and managing
different variants (versions) of it (Klein & Fensel 2001). In other words, ontology evolution is restricted
to the process of modifying an ontology while maintaining its validity, whereas ontology versioning deals
with the process of managing different versions of an evolving ontology, maintaining interoperability
between versions and providing transparent access to each version as required by the accessing element
(data, service, application or other ontology). For a graphical representation of the role of the two fields
in ontology change, see figures 3, 5 in the following subsections.
Another field that is closely related to ontology evolution is ontology debugging. In ontology
debugging, the reason for change is the realization that an ontology contains some kind of logical
contradiction, usually in the form of an inconsistency or an incoherency (Flouris et al. 2006a). Such logical
contradictions are generally undesirable, so ontology debugging attempts to “correct” or “repair” such
problems. Thus, we define ontology debugging as the process of identifying and removing undesirable
logical contradictions (inconsistencies/incoherencies) from an ontology.
The main difference between ontology debugging and ontology evolution is that ontology evolution
attempts to restore logical contradictions (usually inconsistencies) that are caused by the introduction of
some new information, whereas in ontology debugging there is no new information, but the contradiction
is in the ontology to begin with. In fact, the identification and resolution of any inconsistencies that may
arise as a result of a change is one of the most important tasks to be performed during ontology evolution
(Plessers & de Troyer 2006). On the other hand, the problem of ontology evolution can be reduced to the
problem of ontology debugging (Haase et al. 2005). We argue that ideas employed in each field could
also be applied in the other without too much trouble, and cross-fertilization would give rise to significant
progress in both fields.
Ontology debugging is often associated with ontology diagnosis and ontology repair, where ontology
diagnosis refers to the identification of the possible causes of the logical contradiction(s) and ontology
repair refers to the determination of the best way to resolve the logical contradiction(s) (Haase et al.
2005), (Schlobach & Cornet 2003). Under this understanding, ontology debugging consists of these two
processes, namely ontology diagnosis (which identifies the causes of contradictions – note that there may
be more than one alternative causes), and ontology repair (which uses this information to select one of
the alternatives for removing the contradiction). According to (Schlobach & Cornet 2003), the selection
involved in ontology repair requires some understanding of the meaning of the underlying terms, so a
human agent should be involved in the process.

Ontology change: classification and survey 13
4.2 Ontology Evolution
4.2.1 General Discussion on Ontology Evolution
Ontology evolution could be considered as the purest type of ontology change, in the sense that it
deals with the changes themselves (table 1); it is an important problem, as the effectiveness of an
ontology based application heavily depends on the quality of the conceptualization of the domain by the
underlying ontology (Stojanovic et al. 2003), which is directly affected by the ability of an evolution
algorithm to properly adapt the ontology both to changes in the domain (as ontologies often model
dynamic environments (Stojanovic et al. 2002)) and to changes in the domain’s conceptualization (as no
conceptualization can ever be perfect). Figure 3 is a graphical depiction of the role of ontology evolution.
Figure 3 Ontology evolution
As already stated, an ontology is, according to (Gruber 1993a), a specification of a shared concep-
tualization of a domain. Thus, a change may be caused by either a change in the domain, a change in
the conceptualization or a change in the specification (Klein & Fensel 2001). The third type of change
(change in the specification) refers to a change in the way the conceptualization is formally recorded,
i.e., a change in the representation language. This type of change is dealt with in the field of ontology
translation, studied in the previous section (see also table 1). Thus, our definition of ontology evolution
covers the first two types of change only (changes in the domain and changes in the conceptualization).
Both types of changes are not rare. The conceptualization of the domain may change for several
reasons, including a new observation or measurement, a change in the viewpoint or usage of the ontology,
newly-gained access to information that was previously unknown, classified or otherwise unavailable and
so on. The domain itself may also change, as the real world itself is generally not static but evolves over
time. More examples of reasons initiating changes can be found in (Klein & Fensel 2001), (Noy & Klein
2004), (Stojanovic & Motik 2002).
4.2.2 Ontology Evolution Phases
In order to tame the complexity of the problem, six phases of ontology evolution have been identified
in (Stojanovic et al. 2002), occurring in a cyclic loop. Initially, we have the change capturing phase,
where the changes to be performed are determined; these changes are formally represented during the
change representation phase. The third phase is the semantics of change phase, in which the effects of the
change(s) to the ontology itself are determined; during this phase, possible problems that might be caused
to the ontology by these changes are also identified and resolved. The change implementation phase
follows, where the changes are physically applied to the ontology, the ontology engineer is informed of
the changes and the performed changes are logged. These changes need to be propagated to dependent
elements; this is the role of the change propagation phase. Finally, the change validation phase allows
the ontology engineer to review the changes and possibly undo them, if desired. This phase may uncover
further problems with the ontology, thus initiating new changes that need to be performed to improve the
conceptualization; in this case, we need to start over by applying the change capturing phase of a new

14 G.FLOURIS,D.MANA KA NATAS et al.
evolution process, closing the cyclic loop. A similar approach which identifies five phases can be found
in (Plessers & de Troyer 2005).
We will try to illustrate the role of the six phases using an example. The process is initiated by the
change capturing phase, where the need for a change is identified. Three types of change capturing are
described in (Haase & Sure 2004), (Stojanovic & Motik 2002), namely structure-driven, usage-driven and
data-driven; a fourth one (discovery-driven) is used in (Castano et al. 2006b). In our example, suppose
that, for some reason, we decide to remove concept Cfrom our ontology. This decision is taken during
the change capturing phase and the six-phase process of ontology evolution is initiated.
During the change representation phase we determine and formally represent the change(s) that must
be performed in order to remove C. There are two major types of changes, namely elementary and
composite changes (Stojanovic et al. 2002), (Stojanovic & Motik 2002) also called atomic and complex in
(Stuckenschmidt & Klein 2003). Elementary changes represent simple, fine-grained changes; composite
changes represent more coarse-grained changes and can be replaced by a series of elementary changes.
Even though possible, it is not generally appropriate to use a series of elementary changes to replace
a composite one, as this might cause undesirable side-effects (Stojanovic et al. 2002). The proper level
of granularity should be identified at each case. Examples of elementary changes are the addition and
deletion of elements (concepts, properties etc) from the ontology. In our particular example, a simple
Remove Concept operation (which is an elementary operation) should be enough to perform the required
change. This is not always the case though. For example, we might have wished to move the concept C
to some other point in the concept hierarchy. This is represented by a composite change (or by a series of
elementary changes). There is no general consensus in the literature on the type and number of composite
changes that are necessary. In (Stojanovic et al. 2002), 12 different composite changes are identified;
in (Noy & Klein 2004), 22 such operations are listed; in (Stuckenschmidt & Klein 2003) however, the
authors mention that they have identified 120 different interesting composite operations and that the list
is still growing! In fact, the number of definable composite operations can only be limited by setting a
granularity threshold on the operations considered; if we allow unlimited granularity, we will be able to
define more and more operations of coarser and coarser granularity, limited only by our imagination (Klein
& Noy 2003). Thus, creating a complete list of composite operations is not possible, but, fortunately, it
is not necessary either, since a composite operation can always be defined as a series of elementary ones
(Klein & Noy 2003).
Once the required change is identified (i.e., Remove Concept for the concept Cin our example), we
can proceed to the next step of the evolution process, which is the semantics of change phase. At this point,
we should identify any problems that will be caused when the chosen action is actually implemented, thus
guaranteeing the validity of the ontology at the end of the process. In our example, we need (among other
things) to determine what to do with the instances of the concept C; for example, we could delete them
or re-classify them to one of the superconcepts of C. In (Stojanovic et al. 2002), the authors suggest that
the final decision should be made indirectly by the ontology engineer, through the selection of certain
pre-determined evolution strategies, which indicate the appropriate action in such cases. Other (manual or
semi-automatic) approaches are also possible (see (Haase & Sure 2004)). This phase is probably the most
crucial of ontology evolution, because during that phase the direct and indirect changes caused by a given
change request (i.e., the effects and side-effects of the change) are determined.
During the implementation phase, the changes identified in the two previous phases are actually
implemented in the ontology, using an appropriate tool, like, for example, the KAON API (Stojanovic et
al. 2002). Such a tool should have transactional properties, based on the ACID model, i.e., guaranteeing
Atomicity, Consistency, Isolation and Durability of changes (Haase & Sure 2004). It should also present
the changes to the ontology engineer for final verification and keep a log of the implemented changes
(Haase & Sure 2004).
The change propagation phase should ensure that all induced changes will be propagated to the
interested parties. In (Maedche et al. 2003), two different methods to address the problem are compared,
namely push-based and pull-based approaches. Under a push-based approach, the changes are propagated
to the dependent ontologies as they happen; in a pull-based approach, the propagation is initiated only after

Ontology change: classification and survey 15
the explicit request of each of the dependent ontologies. In both (Maedche et al. 2003) and (Stojanovic
et al. 2002) the authors choose to use the former approach (push-based). However, in the Semantic Web
context, this may not always be possible, as the dependent elements may be unknown. Alternatively, one
could avoid this step altogether, by using an ontology versioning algorithm (Klein et al. 2002), allowing
the interested parties to work with the original version of the ontology and update to the newer version
at their own pace, if at all. This alternative is considered more realistic for practical purposes, given the
decentralized and distributed nature of the Semantic Web (Heflin et al. 1999).
Finally, the change validation phase should allow the ontology engineer to review and possibly undo
the changes performed, or initiate a new sequence of changes to further improve the conceptualization of
the domain as represented by the ontology.
The chosen model for ontology evolution allows us to ignore heterogeneity issues. Obviously, any
ontology evolution method will collapse in the face of heterogeneous information, unless coupled with
an algorithm like those discussed in the previous section. However, heterogeneity is not an issue under
the proposed model, because ontology evolution algorithms assume human participation in the process:
during the change representation phase, the ontology engineer specifies the changes to be performed, so it
can be reasonably assumed that these changes will be represented in a suitable language and terminology.
4.2.3 State of the Art in Ontology Evolution
The current state of the art in ontology evolution, as well as a list of existing tools that aid the process can
be found in (Haase & Sure 2004). Some of these tools are simple ontology editors, like Prot´
eg´
e (Noy et al.
2000), which is one of the most popular tools for ontology design and creation but is often also used for
ontology evolution and management. Lately, the functionality of Prot ´
eg´
e has been enhanced (Noy et al.
2006) so as to provide several interesting features useful for both ontology design and evolution. Another
ontology editor often used for ontology evolution is OilEd (Bechhofer et al. 2001). Unlike Prot´
eg´
e, OilEd
is rather restrictive in the sense that it disallows any change that would cause some contradiction (e.g.,
inconsistency, incoherency) in the ontology, rather than taking action against such a contradiction; in
addition, it supports less update operations than Prot´
eg´
e. For a list of desired editor features (with respect
to ontology evolution) and an evaluation of some existing editors in this context see (Stojanovic & Motik
2002).
Apart from Prot´
eg´
e and OilEd, more specialized tools are also available and provide similar editing
features to the user. In some such tools, like KAON (Gabel et al. 2004) and OntoStudio (formerly
OntoEdit (Sure et al. 2003)), the user can define some kind of pre-defined evolution strategies (Stojanovic
et al. 2002) that control how indirect changes (side-effects) will be determined, thus allowing the tool to
calculate side-effects and perform some of the required changes automatically; in this respect, OntoStudio
provides more options for parameterization, but uses a more restricted model and supports less operations
than KAON. Other tools allow collaborative edits, i.e., several users can work simultaneously on the same
ontology (Duineveld et al. 2000), while others support transactional changes (Haase & Sure 2004). In
other works, features related to ontology versioning, undo/redo operations and other helpful utilities are
supported (Duineveld et al. 2000). Some of these tools provide intuitive graphical interfaces that help the
visualization of the process (Lam et al. 2005). For a list of desirable features for ontology evolution tools
refer to (Noy et al. 2006), (Stojanovic & Motik 2002); for a detailed account of related systems refer to
(Duineveld et al. 2000), (Haase & Sure 2004).
The above class of tools focuses on providing an intuitive environment allowing the ontology engineer
to perform the changes in an efficient manner, but provide little or no support for the determination of
the changes’ side-effects, which need to be determined by the ontology engineer as well (either directly
or indirectly). An essential and complementary research path attempts to determine (in an automatic or
semi-automatic way) what the side-effects of each change should be.
Some initial ideas on this issue can be found in (Stojanovic et al. 2002) where evolution strategies
are used for this purpose (see also (Stojanovic & Motik 2002)); this approach is applied in KAON and
OntoStudio. A 5-step workflow which should be followed by an ontology evolution algorithm to determine
the side-effects of a change was proposed in (Konstantinidis et al. 2007). The authors show that a number

16 G.FLOURIS,D.MANA KA NATAS et al.
of existing ontology evolution algorithms follow this pattern and provide a general formal framework
that captures this 5-step pattern. In addition, they claim that their framework can be applied for several
different formalisms and a particular instantiation of it for RDF is presented and evaluated. The proposed
five-step workflow roughly corresponds to the phases of change representation and semantics of change
(phases 2 and 3 respectively), as defined in (Stojanovic et al. 2002).
A more direct approach to the problem can be found in (Magiridou et al. 2005), where a declarative
language for changing (evolving) the data portion of an RDF ontology is introduced; each possible change
in this language has a well-defined set of implied side-effects, which are applied automatically by the
system. A semantics for updating Description Logic (DL) systems (Baader et al. 2002) can be found in
(Roger et al. 2002); this is relevant to ontology evolution, since the family of DLs is one of the most
popular formalisms for ontology representation (Baader et al. 2003). A similar, interesting semantics for
DL updating and some related feasibility results appear in (Liu et al. 2006). In (Haase et al. 2005) the
problem of ontology evolution is addressed by reducing it to the problem of ontology debugging.
An alternative, novel approach that is constantly gaining ground in the area, uses belief change
(G¨
ardenfors 1992a) techniques to handle ontology evolution. In such works, popular belief change
techniques, normally inapplicable for ontology representation languages, are being modified so as to
be able to deal with ontologies and ontology evolution. This methodology is particularly applicable to
the most important (and difficult) phase of ontology evolution, namely the semantics of change phase
(Qi et al. 2006b) and constitutes another approach towards the automatic determination of the changes’
side-effects.
Arguments in favor of this research path and some results related to its feasibility for a particular class
of belief change theories, based on the AGM postulates (Alchourron et al. 1985), have appeared in a
series of papers (Flouris 2006), (Flouris et al. 2006a), (Flouris & Plexousakis 2006), (Flouris et al. 2004),
(Flouris et al. 2005), (Flouris et al. 2006b). A similar, but less mature, proposal appears in (Kang & Lau
2004).
A formalization of updates in RDF, which is inspired by belief change ideas, appears in (Gutierrez et
al. 2006); this formalization allows automatically determining what the result of an update upon an RDF
ontology should be. In (Lee & Meyer 2004), a preference ordering (inspired by similar orderings in belief
change) is used to determine how new information should be added to ontologies represented using a
particular DL (namely, ALU).
In (Ribeiro & Wassermann 2007) the belief change idea of kernel operators (Hansson 1994) is used,
by adapting it for knowledge representation formalisms that do not allow axiom negation (such as the
formalisms used in ontologies); this allows kernel operators to be used for ontology evolution. Other works
(Halaschek-Wiener & Katz 2006) combine ideas from ontology debugging (in particular, the approach of
(Kaluyanpur 2006)) and kernel operators, in order to propose an ontology evolution operator for OWL
ontologies.
Another interesting application of belief change results for the purposes of ontology evolution was
spawned by the work of (Meyer et al. 2005), where the authors attempt to recast the maxi-adjustment
algorithm (Benferhat et al. 2004), originally introduced for propositional knowledge integration, in the
DL context. The maxi-adjustment algorithm allows the elimination of any inconsistencies that could arise
in a stratified propositional KB after its expansion with a new proposition. In (Meyer et al. 2005) this idea
was applied for stratified DL ontologies, leading to an ontology debugging algorithm. Subsequently, in
(Qi et al. 2006a), it was adapted and applied to the ontology evolution context, leading to an evolution
algorithm for stratified DL ontologies. In (Qi et al. 2006b) the theoretical backbone of this approach was
presented, leading to a set of postulates, inspired by the belief change postulates of (Katsuno & Mendelzon
1990). Unfortunately, this entire line of work requires the introduction of disjunctive DLs, a certain DL
extension for which axiom disjunction is allowed (Meyer et al. 2005); this constitutes a departure from
the classical DL model, limiting the applicability of the approach.
An interesting variation of the problem of ontology evolution appears in (Foo 1995), (Wassermann
1998), (Wassermann & Ferm´
e 1999). In these papers, evolution is addressed from a different standpoint,
in which the evolving objects (and therefore the main objects of study) are the concepts; this viewpoint

Ontology change: classification and survey 17
is quite different from the standard one in which the evolving object is the ontology as a whole. The
term ontology revision is often used to refer to this research path (Foo 1995). Another variation, used for
multimedia ontology evolution, appears in (Castano et al. 2006b), (Castano et al. 2007), where ontology
matching approaches are used to assist the ontology engineer in defining new concepts to be added to
the ontology. Ontology evolution does not normally deal with the evolution of an ontology’s metadata;
however, this is the case in (Maynard et al. 2007), which addresses the problem of identifying the effects of
ontological changes to the ontology’s metadata and vice-versa, constituting another variation of ontology
evolution.
4.3 Ontology Debugging
As already mentioned, the purpose of ontology debugging is the identification and resolution of logical
contradictions (see figure 4). Logical contradictions can occur due to several reasons or actions, such
as modeling errors, ontology merging or integration, migration from other formalisms and ontology
evolution (Haase & Qi 2007).
Figure 4 Ontology debugging
Standard reasoners are of little help for the task of ontology debugging, because, even though they
can identify the existence of a contradiction, they provide little support for resolving and eliminating it
(Meyer et al. 2006). The resolution of contradictions is a challenging task for ontology modelers (Lam
et al. 2006) which cannot be undertaken manually, especially when expressive ontological languages are
used. Therefore, a more powerful approach is required in order to identify the part(s) of the ontology that
led to the contradiction (Meyer et al. 2005).
In principle, there are two types of logical contradictions that can occur in ontologies: inconsistency
and incoherency. Often, the term inconsistency is used to refer to both types of contradictions; here we
follow the terminology of (Flouris et al. 2006a), where an inconsistent ontology was defined as one which
has no model, and an incoherent ontology was defined as one which contains an unsatisfiable concept
(i.e., a concept which can have no instances).
Under the above definition, inconsistency corresponds to the standard meaning of the term in logical
formalisms and indicates an ontology with trivial consequences (to be exact, anything follows from an
inconsistent ontology); as a result, an inconsistency renders the ontology unusable, so ontology debugging
techniques should be applied to avoid this trivialization. In the ontological context, incoherency is also
important because unsatisfiable concepts often indicate a conceptualization problem in the ontology and
should thus be avoided. In fact, most of the approaches in ontology debugging deal with incoherency
rather than inconsistency (Meyer et al. 2006).
In (Haase et al. 2005) four alternative methods for dealing with inconsistency are discussed. In par-
ticular, inconsistencies can be avoided (using ontology evolution techniques), corrected (using ontology
debugging techniques), ignored (using smart reasoning techniques that will allow meaningful reasoning

18 G.FLOURIS,D.MANA KA NATAS et al.
in the presence of inconsistencies) or addressed indirectly (using versioning techniques that prevent
incompatibilities between interrelated ontologies); of course, the latter technique is applicable only
when the inconsistency is caused by the interaction of ontologies, i.e., due to some ontology using a
version of another ontology with which it is not compatible. In this subsection, we consider the second
case (correcting inconsistencies using ontology debugging), whereas ontology evolution and versioning
are discussed in other subsections of section 4; approaches that allow reasoning in the presence of
inconsistencies are outside the scope of this paper and will not be further discussed.
An interesting overview of the ontology debugging field can be found in (Haase & Qi 2007); in that
paper, various different approaches that are directly or indirectly related to debugging are classified and
critically evaluated along various criteria. One of the conclusions of (Haase & Qi 2007) is that, non-
surprisingly, none of the surveyed approaches is universally applicable for all application scenarios, but
different approaches are good for different purposes.
Most of the works related to the field of ontology debugging actually deal with the problem of
diagnosis, i.e., the identification of the causes of the contradiction, rather than the resolution of the
contradiction. This is because of the general agreement that the best thing an automated system can do is to
propose the alternative ways to repair an ontology, but it’s up to a human expert to select the appropriate
way to resolve the contradiction (Schlobach & Cornet 2003). Thus, research efforts have focused on
the problem of diagnosis, leaving the problem of selecting the best way to resolve a contradiction (i.e.,
ontology repair) to human experts.
Many approaches to ontology debugging use some tableau-based algorithm to pinpoint the cause of
an incoherence. One of the most influential approaches to ontology debugging was given in (Schlobach
& Cornet 2003), where a tableau-based algorithm for handling incoherencies in DLs (in particular, for
the DL ALC) was presented. Some useful definitions were also provided there. In (Kalyanpur 2006),
(Kalyanpur et al. 2006) the tableau-based algorithm of (Schlobach & Cornet 2003) was extended to be
able to indicate specific parts of axioms that are responsible for incoherencies in OWL ontologies. A
different tableau-based algorithm for debugging OWL ontologies is presented in (Plessers & de Troyer
2006).
In (Meyer et al. 2006), another tableau-based approach to handle incoherencies for ontologies
represented using a particular DL (namely, ALC) is proposed, and some preliminary experimental results
on its use are reported. Inspired by this work, (Lam et al. 2006) propose a refinement of the above
technique which allows a more fine-grained approach to the problem by tracing the parts of the axioms
that are responsible for an incoherency; this gives us the ability to restore coherency by eliminating parts
of axioms, rather than entire axioms.
A heuristic approach for debugging OWL DL ontologies appears in (Wang et al. 2005); this approach
is based on five steps that are sequentially executed following the identification of an incoherency by a
standard OWL reasoner and allow the pinpointing of the source of the incoherence. In (Schlobach 2005),
a framework for debugging incoherent terminologies is proposed, which is based on traditional model-
based diagnosis; a major advantage of this framework is that it can be proved to work with a variety of
formalisms, including very expressive ones, unlike most approaches which are tailored for particular (and
usually simple) DLs. The approach that appears in (Friedrich & Shchekotykhin 2005) is based on similar
foundations and enjoys the same general applicability.
All the above works deal with incoherency debugging. In contrast, (Qi & Pan 2007) deals with
inconsistency debugging; their approach consists in developing an algorithm that allows reasoning in the
presence of inconsistencies and applying it for debugging inconsistent stratified DL ontologies. Similarly,
(Meyer et al. 2005), despite its title, does not deal with ontology integration, but with ontology debugging;
in particular, the authors use the conjunctive maxi-adjustment algorithm of (Benferhat et al. 2004) in order
to provide a refined algorithm for debugging stratified DL ontologies. This algorithm also deals with the
resolution of inconsistency, rather than incoherency, and is applicable for disjunctive DLs only.

Ontology change: classification and survey 19
4.4 Ontology Versioning
Following a change upon an ontology, ontology versioning algorithms come into play (see table 1 and
figure 5). Ontology versioning typically involves the storage of both the old and the new version of
the ontology and takes into account identification issues (i.e., how to identify the different versions of
the ontology), the relation between different versions (i.e., a tree, or more generally, a directed acyclic
graph, of versions resulting from the various ontology modifications) as well as some compatibility
information (i.e., information regarding the compatibility of any pair of versions of the ontology). It has
been argued that ontology versioning, and, in particular, compatibility determination, cannot be performed
automatically (Heflin & Pan 2004), so ontology versioning tools should aim at assisting an ontology
engineer specifying the related information.
Figure 5 Ontology versioning
Several non-trivial problems are associated with ontology versioning. For example, any ontology
versioning algorithm should be based on some type of identification mechanism to differentiate between
various versions of an ontology. This task is not as easy as it may seem; for example, it is not clear
when two ontologies constitute different versions. Should any change in the file that stores the ontology
specification constitute the creation of a new version? When a concept specification changes, but the new
specification is semantically equivalent to the original one, should this constitute a new version? More
generally, when the ontology changes syntactically, but not semantically, should this constitute a new
version? These and similar problems are dealt with in (Heflin et al. 1999), (Klein et al. 2002).
Another desirable property of an ontology versioning system is the ability to allow transparent access
to different versions of the ontology, by automatically relating versions with data sources, applications
and other dependent elements (Klein & Fensel 2001). Other issues involved is the so-called “packaging

20 G.FLOURIS,D.MANA KA NATAS et al.
of changes” (Klein et al. 2002) as well as the different types of compatibility and how these are identified
(Klein & Fensel 2001).
Another related problem is the introduction of a certain version relation between ontological elements
(such as classes) appearing in different versions of the ontology and the properties that such a relation
should have. This relation is called a change specification in (Klein & Fensel 2001) and its role is to make
the relationship between different versions of ontological elements explicit. Using this relation, one can
identify the changes that any given element went through between different versions; in addition, a version
relation should include certain meta-data regarding these changes (Klein et al. 2002). In (Plessers & de
Troyer 2005) this relation is stored using a version log which is actually a specially designed ontology
storing the different versions of each element, as well as the relation between them and some related meta-
data. Similar considerations led to the definition of migration specifications (Zhang et al. 2003), which
associate concepts between different versions of an ontology after a change has been performed. A similar
approach is presented in (Noy et al. 2006), where the CHAO ontology is introduced in order to provide a
framework for explicitly storing the changes performed to the ontology.
As an aid to the task of ontology versioning, certain tools have been developed which automatically
identify the differences between ontology versions; unfortunately, most such tools provide information at
the level of elementary changes only (Klein & Noy 2003). For example, PROMPTDIFF (Noy & Musen
2002), (Noy & Musen 2004), (Noy et al. 2004) uses certain heuristics to compare different versions of
ontologies and outline their differences, by producing a structural diff between them; OntoView (Klein
et al. 2002) contains a tool similar to PROMPTDIFF, whose output is a certain ontology of changes,
i.e., a specially designed ontology that stores the identified types of changes, thus providing a kind of
mapping between versions. In (Zeginis et al. 2007) four different ways (“delta functions”) to compare RDF
ontologies are provided; these functions are based on the ontologies’ semantics and some of them take into
account the inference mechanism of RDF. In the same paper, these delta functions are compared along
several dimensions, like correctness, size etc. The results of comparison tools can be greatly improved if
combined with an explicit description of (some of) the changes that the ontology went through, e.g., using
the CHAO ontology (Noy et al. 2006) which was developed as an add-on for PROMPTDIFF.
A survey on the different ways that can be used to represent a set of changes, as well as the relations
and possible interactions between such representations can be found in (Klein & Noy 2003). In the same
paper, a standard ontology of changes is proposed, containing both basic (i.e., elementary) and complex
(i.e., composite) operations. A similar ontology of changes is proposed in (Plessers & de Troyer 2005),
where the changes are identified through a version log stored in this ontology of changes.
SemVersion (V ¨
olkel et al. 2005) is an RDF-based ontology versioning system that separates the
management aspects of the problem from the versioning core functions. An interesting proposal for a
theoretical foundation for ontology versioning appears in (Heflin & Pan 2004). A method to identify
compatibility between ontology versions is presented in (Heflin et al. 1999), (Heflin & Hendler 2000)
where the SHOE language (Luke et al. 1997) is used to make backward compatibility between versions
explicit in a machine-readable format, allowing a computer agent to determine compatibility between
versions. This is an indirect approach to the problem of ontology versioning, as it allows the computer
agent to determine autonomously which version to use, as opposed to (Klein & Fensel 2001), (Klein et al.
2002), where a more direct and centralized path is taken.
In (Huang & Stuckenschmidt 2005), a temporal logic approach is used to allow access and reasoning
in different versions of an ontology. Ideas from temporal databases are also employed in (Plessers et
al. 2005), where a methodology of capturing the evolution of ontology instances with the purpose of
performing efficient ontology versioning is described. A temporal DL, coupled with a temporal query
language, is presented in (Keberle et al. 2007); the authors provide the means to record the evolution of
each ontological element through time as well as to determine the status of each element at any given time
point (using their temporal DL and query language). This way, one provides an indirect way to support
versioning (called ontology evolution analysis in (Keberle et al. 2007)), as we are able to determine the
status of the ontology at any given time in the past.

Ontology change: classification and survey 21
5 Ontology Integration and Merging
5.1 Definitions and Discussion on Ontology Integration and Merging
In short, both ontology integration and ontology merging refer to the creation of a new ontology based on
the information found in two or more source ontologies. As we will see however, the two terms refer to
slightly different research areas. Unfortunately, the exact meaning of each term is not always clear in the
literature, as they are often used interchangeably. This situation has led to a certain amount of confusion
regarding the exact boundaries of each field.
In this work, we will define these terms along the lines of (Pinto et al. 1999), which was an attempt to
disambiguate between different uses of the term “ontology integration”. Three different uses of the term
were identified in that paper. The first refers to the composition (via reuse) of ontologies covering loosely
related (i.e., similar) domains (subjects); this is mainly used when building a new ontology that covers all
these subjects. The term ontology integration has been reserved for this process. The second use of the
word integration refers to the combination of ontologies covering highly overlapping or identical domains;
this process is used to fuse ontologies that contain information about the same subject into one large (and
hopefully more accurate) ontology. The term ontology merging was attached to this interpretation. Finally,
the third use of the term refers to the development of an application that uses one or more ontologies; the
more appropriate term ontology use was reserved for this process. In this work, we will be interested only
in the first two research areas, namely ontology integration and merging, because ontology use could not
be considered a type of ontology change.
There are certain subtle differences between the processes of ontology integration and merging
(see table 1). Ontology integration is mainly applied when the main concern is the reuse of other
ontologies. The domain of discourse of the new ontology is usually more general than the domain of
any of the sources, and integration often places the different source ontologies in different modules that
comprise the resulting ontology; these modules are only loosely interconnected in the final result. Using
ontology integration, the process of ontology development can become more efficient, because previously
developed and tested ontologies can be reused as building blocks for the creation of the new (and more
general) ontology in a modular manner. For a graphical depiction of the process of ontology integration,
see figure 6.
On the other hand, in ontology merging, the focus is on creating an ontology that combines information
on a given topic from different sources. In this case, the information from the source ontologies is greatly
intermingled, so it’s difficult to identify the part(s) of the final ontology that resulted from each source
ontology. This process is useful when each source ontology models the domain under question only
partially, or under a particular viewpoint, and we would like to combine the information into a larger,
more accurate and complete ontology. For a graphical depiction of the process of ontology merging, see
figure 7. A more extensive discussion on the differences between integration and merging can be found in
(Pinto et al. 1999).
5.2 Alternative Understandings of the Terms
It is a common practice in the literature to consider heterogeneity resolution to be an internal part of
ontology merging or integration (De Bruijn et al. 2004), (Choi et al. 2006), (Heflin et al. 1999), (Pinto
et al. 1999). This is a reasonable choice, because in most cases the fused ontologies come from different
sources, so they are generally heterogeneous in terms of vocabulary, syntax, representation etc. Therefore,
the task of relating (matching) the heterogeneous elements of the source ontologies constitutes the first
step (and a major part) of the task of ontology merging or integration (Choi et al. 2006). This is mostly
true in merging, where the domain of discourse is (almost) identical.
Unfortunately however, this has led to even more confusion on the exact meaning of the terms, as
ontology merging (or integration) are often identified with the process of resolving heterogeneity issues
and several works consider ontology merging (or integration) and matching to be variations of the same
problem. In (Noy & Musen 2000), (Noy & Musen 1999a), for example, ontology merging is defined
as the process of creating a new, coherent ontology that includes information from two or more source

22 G.FLOURIS,D.MANA KA NATAS et al.
Figure 6 Ontology integration
ontologies. In these papers, ontology merging is implicitly assumed to include the process of resolving
any possible heterogeneities between the merged ontologies, even though this is not apparent in their
definition. In addition, under their understanding, the difference between ontology merging and matching
is that ontology merging results in the creation of a new ontology, whereas in ontology matching the
merged ontologies persist, with links established between them.
A similar use of the term can be found in (McGuiness et al. 2000), whereas, in (Heflin et al. 1999), the
same research area is described using the term “ontology integration”. According to (Lee & Meyer 2004),
ontology merging amounts to making sure that different agents use the same terms in identical ways
(in a manner similar to ontology matching). In (Kalfoglou & Schorlemmer 2003) ontology integration
is defined along our lines, i.e., as the process of combining ontologies to build new ones, but whose
respective vocabularies are usually not interpreted in the same domain of discourse.
However, it is important to note that simply resolving the heterogeneity issues between two ontologies
is not sufficient for successful integration (or merging); recall that different ontologies may encode
different viewpoints regarding the real world, thus several conceptual differences are bound to exist,
even if the same terminology is used. This is reminiscent of how beliefs held by different people
are often different (and in some cases contradictory), even if a common terminology is agreed upon.
Similarly, modeling conventions and choices may be different; one example of a modeling choice that
often depends on personal taste or convention is whether to model a certain distinction between similar
elements by introducing separate classes or by introducing a qualifying attribute relation in one class
(Chalupsky 2000); an example of this particularly difficult situation appears in figure 8. Such modeling

Ontology change: classification and survey 23
Figure 7 Ontology merging
differences need to be taken into account when selecting what to keep from each ontology during the
integration or merging process. Reckless inclusion of ontology elements from the source ontologies (even
when homogeneous) is likely to lead to a problematic, invalid, contradictory, incoherent or inconsistent
ontology. For this reason we argue that ontology merging (and integration) are not simply a variation of
ontology matching.
A slightly different use of the term “ontology integration” appears in (Calvanese et al. 2002), where
the term is used to refer to the process of combining a number of local ontologies in order to build a
global one, with the purpose of being able to answer queries over the local ontologies, using the unified
terminology imposed by the global ontology. Again, the focus is on heterogeneity resolution and a formal
framework for defining and exploiting mappings with respect to query answering is proposed.
In (De Bruijn et al. 2004), ontology merging is defined as the process of unifying two or more ontolo-
gies; this definition poses no restrictions as to the domain of the unified ontologies, so it includes both
ontology integration and merging. In the same paper, two approaches to ontology merging are identified:
the union approach, where the merged ontology is the union of all entities in both source ontologies,
taking into account heterogeneity issues, and the intersection approach, where only overlapping parts of
the source ontologies are included in the result.

24 G.FLOURIS,D.MANA KA NATAS et al.
Figure 8 A modeling convention that needs to be taken into account in ontology integration and merging
5.3 State of the Art in Ontology Integration and Merging
According to (Chalupsky 2000), the process of merging can be broken down in five steps. During the first
step, we identify the semantic overlap between the source ontologies; during the second, we devise ways
(transformations) that will bring the sources into mutual agreement in terms of terminology, representation
etc. In the third step, we apply these transformations, so we can now take the union of the sources, which is
the fourth step of the process. The final step consists of evaluating the resulting ontology for consistency,
uniformity, redundancy, quality of conceptualization etc; this evaluation might force us to repeat some or
all of the above steps. We believe that the same generic steps are necessary for integration as well, but
they should be adapted to the different properties of the process. In (Chalupsky 2000), it is claimed that
OntoMorph (a translation tool described in the same paper) is a necessary part of any merging algorithm,
as it facilitates the design and implementation of the transformations used in the merging process (steps
2, 3 respectively).
An extensive list of works related to ontology merging can be found in (De Bruijn et al. 2004),
(Choi et al. 2006), (Pinto et al. 1999). The main tools used for ontology merging are PROMPT (Noy
& Musen 2003), (Noy & Musen 2000) and Chimaera (McGuiness et al. 2000). These tools use a semi-
automatic approach focused on suggesting how elements from the source ontologies should be merged in
the resulting ontology. The final choice relies on the ontology engineer.
Some ideas on ontology merging (called integration there) in the context of the SHOE language can
be found in (Heflin et al. 1999); however, this work is focused on the part of merging that deals with
heterogeneity resolution. The FCA-MERGE algorithm (Stumme & Maedche 2002) performs ontology
integration in a very efficient way, but is based on certain strong assumptions. In (Noy & Musen 1999a)
some interesting connections of object-oriented programming with the problem of ontology merging
are uncovered. An interesting theoretical approach to ontology integration (also applicable to ontology

Ontology change: classification and survey 25
merging) appears in (Calvanese et al. 2002), which focuses on the formal definition of mappings between
the resulting and the source ontologies and how these mappings can be exploited for query answering;
another theoretical approach to ontology merging can be found in (Bench-Capon & Malcolm 1999).
It should be emphasized that, to the best of our knowledge, all currently available tools use manual
or semi-automatic methods to perform merging (or integration), guiding the user through the different
steps of the process via some intuitive interface. The automatic processes behind ontology merging and
integration are not yet well-understood: in (Pinto et al. 1999), it is claimed that “ontology merging is
currently more of an art”. Ontology integration (and merging) can benefit from advances in the related field
of database schema integration (and merging); an indicative list of relevant systems (performing database
schema integration) includes Quete (Kondylakis, 2006), BACIIS (Ben-Miled et al. 2005), TAMBIS
(Stevens et al. 2000), ONTOFUSION (Perez-Ray et al. 2006), MECOTA (Goasdoui et al. 2000) and
SEMEDA (Kohler et al. 2003). Further details on these systems are omitted, as they deal with databases
rather than ontologies, so they are outside the scope of this paper.
Even though the issue of evaluating ontology integration and merging techniques is still open (Stumme
& Maedche 2002), certain comparison attempts have been made. In (Lambrix & Edberg 2003), the authors
perform a comparison between PROMPT and Chimaera in the context of bioinformatics. In (Noy &
Musen 2000), the same two tools are compared with the generic Prot´
eg´
e (Noy et al. 2000), whereas
(McGuiness et al. 2000) compares, in the context of merging, the efficiency of a simple plain-text editor,
the Ontolingua editor and the specialized tool Chimaera (described in the same paper). These comparisons
are made from a certain standpoint; a general, objective comparison is difficult, as it is not clear how the
utility of such tools could be measured (McGuiness et al. 2000).
6 Conclusion
We performed a literature review covering all the diverse types of ontology change, focusing on classifi-
cation and breadth of coverage rather than on depth of analysis. Our aim was to propose a terminology in
an area that is plagued by the presence of underspecified and confusing terms. A comprehensive summary
of our results can be found in table 1. The proposed terminology was not introduced in an arbitrary
manner, but was based on similar previous attempts (like, for example, (Kalfoglou & Schorlemmer 2003),
(Pinto et al. 1999)) and on the most common uses of the terms in the literature. In addition to the proposed
terminology, we discussed alternative definitions and uses of the terms that have appeared in the literature.
This would minimize the amount of effort required for a reader in order to follow a work that uses an
interpretation different from the one proposed here and provides a more complete view of the area of
ontology change.
In this paper, we used the term “ontology change” in a very general sense, so as to engulf any
approach that is related to the dynamics of ontologies, including works that are indirectly related to the
problem, such as those dealing with heterogeneity resolution or the management of different versions of
an evolving ontology. We identified 10 research subfields which can fit under the general definition of
ontology change, namely ontology mapping, morphism, matching, articulation, translation, evolution,
debugging, versioning, integration and merging. In short, ontology mapping, morphism, matching,
articulation and translation address the problem of heterogeneity resolution, ontology evolution deals
with the incorporation of new knowledge in an ontology, ontology debugging seeks techniques that would
remove contradictions from an ontology, ontology versioning addresses the management of different
versions of an evolving ontology and ontology integration and merging deal with the fusion of knowledge
from different ontologies into a single one.
Many of the above fields are closely related; as a result, many works and approaches deal with more
than one of them. For example, several works addressing the problem of ontology merging or integration
also deal with heterogeneity issues, and many ontology evolution algorithms address versioning or
debugging issues (see also table 2). This is the primary reason for the confusion that often exists as to
the meaning of the various terms discussed here; the clarification of this confusion constitutes the main
motivation behind this survey.

26 G.FLOURIS,D.MANA KA NATAS et al.
The second purpose of this survey was an overview of the ontology change literature, focusing on
breadth of coverage rather than depth of analysis. In this respect, we briefly described several works
related to ontology change and classified them according to the problem(s) that they address (see table 2
for a summary). Our analysis indicated the main trends in each subfield of ontology change and showed
the main strengths and weaknesses of the dominating research paradigms.
More specifically, as far as heterogeneity resolution is concerned, it was made clear that the problem is
very difficult and multi-faceted. Therefore, despite the persistent efforts of many capable researchers, the
results are still not entirely satisfactory; many people believe that the process cannot be fully automated
and that the more realistic path is to resort to semi-automatic methods that would significantly reduce
(but not eliminate) human intervention. At the moment, there are several available systems that perform
mapping, matching or some other flavor of heterogeneity resolution, and many complementary approaches
to address the problem have been proposed (see figure 2 for a summary).
In ontology evolution, two major research paths can be identified. The first focuses on aiding the user
performing changes through some intuitive interface providing useful editing features; such approaches
resemble an ontology editor, even though they often provide many more features than a simple ontology
editor would. The second research path focuses on the development of automated methods to determine
the effects and side-effects of any given update request; this approach often borrows ideas from the related
field of belief change. The first class of tools is more mature at the moment, but the second approach is
more promising and more interesting from a research point of view; for this reason, it is gaining increasing
attention during the last few years. The two research paths are complementary, as results from the second
could be applied to the first in order to further improve the quality of the front-end editing tools; similarly,
automated approaches are of little use unless coupled with tools that address the practical issues related
to evolution, like support for multi-user environments, transactional issues, change propagation etc.
Ontology debugging is a recent field; (Schlobach & Cornet 2003) was practically the first approach
dealing with the problem. This field has strong connections to the ontology evolution field (especially
the second research path described above), so many ontology evolution approaches could be adapted to
solve problems related to ontology debugging and vice-versa. At the moment, mainly the problem of
incoherence is addressed in ontology debugging, but the field is in need of approaches that could handle
both inconsistency and incoherency.
Often, ontology versioning approaches do not deal with the problem of versioning itself (recording
and storing versions) but with peripheral problems, like the identification and recording of the changes
between versions or the determination of compatibility between versions. This is normal, given that these
problems constitute subproblems of ontology versioning and are more interesting, from a research point of
view, than the (purely technical) problem of deciding how to record and store versions. There is a number
of interesting results in this respect, but most problems related to versioning remain open.
Most approaches to ontology merging and integration deal with the part of merging and integration that
is related to heterogeneity resolution. Even though this is an important aspect of the problem, one should
not underestimate the difficulties related to the merging (or integration) of homogeneous ontologies; given
that different ontologies encode different viewpoints, the reckless addition of facts from one ontology to
the other would, most likely, lead to an inconsistent, incoherent or otherwise invalid ontology. We believe
that this aspect of merging and integration would be an interesting field for future research.
In general, ontology change is a complex and multi-faceted problem, subparts of which are being
addressed by different disciplines. We hope that our work will prove helpful towards the clarification of
the terminology, as well as the boundaries and relations between these disciplines. We aim this work to
serve as a starting point for researchers interested in any of the many facets of the problem.
Acknowledgements
This paper is a heavily revised and extended version of (Flouris et al. 2006c). The authors would like to
thank Panos Constantopoulos, Vassilis Christophides and Nicolas Spyratos for helpful comments on an
earlier draft of this work, and the anonymous reviewers for their helpful comments. This work was partly
carried out during the first author’s tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.

Ontology change: classification and survey 27
Appendix
Table 2 contains a comprehensive classification of the works referenced in this paper. Using this table, the
reader can get a quick glimpse of the papers related to any specific subfield of ontology change, as well
as to determine the relevance of any given work to any particular discipline; for a more accurate account
of the contribution of each paper to the respective discipline, please refer to the appropriate section of
this survey. Notice that referenced works that are not directly related to ontology change have not been
included in the table.
Table 2: Referenced papers and related ontology change subfields
Referenced Paper
Mapping
Morphism
Matching
Articulation
Translation #1
Translation #2
Evolution
Debugging
Versioning
Integration
Merging
Aumueller et al. 2005 X
Avesani et al. 2005 X X
Bechhofer et al. 2001 X
Bench-Capon & Malcolm 1999 X
Bernstein et al. 2004 X
De Bruijn et al. 2004 X X X X
Calvanese et al. 2002 X X
Castano et al. 2007 X
Castano et al. 2006a X
Castano et al. 2006b X X
Chalupsky 2000 X X
Choi et al. 2006 X X X X
David et al. 2006 X
Do & Rahm 2002 X
Do et al. 2002 X X
Doan et al. 2000 X
Doan et al. 2002 X
Duineveld et al. 2000 X
Ehrig et al. 2005 X
Euzenat et al. 2004 X X
Euzenat & Shvaiko 2007 X X X
Ferrara 2004 X X
Flouris 2006 X
Flouris et al. 2006a X
Flouris & Plexousakis 2005 X X X X X X X X X
Flouris & Plexousakis 2006 X
Flouris et al. 2004 X
Flouris et al. 2005 X
Flouris et al. 2006b X
Flouris et al. 2006c X X X X X X X X X
Friedrich & Shchekotykhin 2005 X
Foo 1995 X
Gabel et al. 2004 X
Giunchiglia et al. 2004 X
Giunchiglia et al. 2006 X
continued on next page

28 G.FLOURIS,D.MANA KA NATAS et al.
Table 2: Referenced papers and related ontology change subfields (cont.)
Referenced Paper
Mapping
Morphism
Matching
Articulation
Translation #1
Translation #2
Evolution
Debugging
Versioning
Integration
Merging
Gutierrez et al. 2006 X
Haase et al. 2005 X X
Haase & Qi 2007 X
Haase & Stojanovic 2005 X
Haase & Sure 2004 X X
Halaschek-Wiener & Katz 2006 X
Heflin & Hendler 2000 X X
Heflin et al. 1999 X X X
Heflin & Pan 2004 X
Hu & Qu 2006 X
Huang & Stuckenschmidt 2005 X
(IBM Corporation) X
Kalfoglou & Schorlemmer 2003 X X X X X X X
Kalyanpur 2006 X
Kalyanpur et al. 2006 X
Kang & Lau 2004 X
Keberle et al. 2007 X
Klein & Fensel 2001 X X
Klein et al. 2002 X
Klein & Noy 2003 X X
Kondylakis et al. 2006 X X
Konstantinidis et al. 2007 X
Lam et al. 2006 X
Lam et al. 2005 X
Lambrix & Edberg 2003 X
Lee & Meyer 2004 X
Liu et al. 2006 X
Luke et al. 1997 X
Madhavan et al. 2005 X
Madhavan et al. 2001 X
Maedche et al. 2003 X
Manakanatas & Plexousakis 2006 X X
Magiridou et al. 2005 X
Maynard et al. 2007 X
McGuiness et al. 2000 X X X X
Melnik et al. 2002 X
Meyer et al. 2005 X
Meyer et al. 2006 X
Mitra et al. 2005 X X
Mocan et al. 2006 X
Noy et al. 2006 X X
Noy et al. 2000 X
continued on next page

Ontology change: classification and survey 29
Table 2: Referenced papers and related ontology change subfields (cont.)
Referenced Paper
Mapping
Morphism
Matching
Articulation
Translation #1
Translation #2
Evolution
Debugging
Versioning
Integration
Merging
Noy & Klein 2004 X
Noy et al. 2004 X
Noy & Musen 1999a X X
Noy & Musen 1999b X X
Noy & Musen 2000 X X
Noy & Musen 2002 X
Noy & Musen 2003 X X X
Noy & Musen 2004 X
Palmisano et al. 2006 X X
Pan et al. 2005 X X
Pinto et al. 1999 X X
Plessers & de Troyer 2005 X X
Plessers & de Troyer 2006 X
Plessers et al. 2005 X X
Qi et al. 2006a X
Qi et al. 2006b X
Qi & Pan 2007 X
Rahm & Bernstein 2001 X
Ribeiro & Wassermann 2007 X
Roger et al. 2002 X
Schlobach 2005 X
Schlobach & Cornet 2003 X
Shvaiko & Euzenat 2005 X
Shvaiko et al. 2006 X
Stoilos et al. 2005 X X
Stojanovic et al. 2002 X
Stojanovic et al. 2003 X
Stojanovic & Motik 2002 X
Stuckenschmidt & Klein 2003 X
Stumme & Maedche 2002 X
Sure et al. 2003 X
Svab et al. 2007 X X
V¨
olkel et al. 2005 X
Wang et al. 2005 X
Wassermann 1998 X
Wassermann & Ferm´
e 1999 X
Yatskevich 2003 X X
Zeginis et al. 2007 X
Zhang et al. 2003 X
Zhdanova & Shvaiko 2006 X
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