Ontology Alignment: State of the Art, Main Trends and Open Issues

Ontology Alignment: State of the Art, Main Trends and Open Issues

Tatyana Ivanova (Technical University of Sofia, Bulgaria)
Copyright: © 2010 |Pages: 19
DOI: 10.4018/jkss.2010100102
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A grand number of ontologies have been developed and are publicly accessible on the Web making techniques for mapping between various ontologies more significant. Research has been made in the area of ontology alignment, a grand number of approaches, algorithms, and tools have been developed in recent years, but are still not “perfect” and excellent knowledge. In this article, the author makes an overall view of the state of ontology alignment, including the latest research, comparing many approaches, and analyzing their strengths and drawbacks. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which can be used for ontology mapping in all cases, making knowledge about developed ontology mapping methods and their clear classification needed.
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Ontology matching is the process of finding correspondences between entities belonging to different ontologies: classes, attributes, relationships, or value of attributes. The task of ontology alignment can be described as follows: given two ontologies, each of which describes a set of elements (classes, properties, rules, etc.), find the relationships (equivalence or subsumption), holding between these elements. More formally, an alignment between two ontologies O1 and O2 is a set of 4-tuple correspondences of type (e1, e2, R, n), where:

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    e1 and e2 are the entities (e.g., formulas, terms, classes, individuals) of O1 and O2 between which a relation is asserted by the correspondence;

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    R is the relation, between e1 and e2, asserted by the correspondence. This relation can be a simple set-theoretic relation (applied to entities seen as sets or their interpretation seen as sets), a fuzzy relation, a probabilistic distribution over a complete set of relations, a similarity measure, etc.;

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    n is a degree of confidence in that correspondence (measure of the trust in the fact that the correspondence is appropriate);

Definitions of many concepts, related to ontology mapping can be found in (Choi et al., 2006). Much research has been made in the area of ontology alignment, a grand number of approaches, algorithms and tools have been developed in resent years, but mo one is “perfect” and choosing an alignment approaches, algorithms or tools for particular tack can be very difficult. Even when the needs are very clear, there are many criteria that can be used for choosing an adequate technique or matcher and all needed criteria cannot be assessed in the same way. It is also difficult to obtain all the information about each approach, technique or tool.

In this paper, I analyze and compare ontology mapping approaches (on the base mainly of results from a literature survey and according to many important for research and practical application criteria), merge and extend earlier good classifications by adding the newest research approaches, and discuss the principles behind ontology mapping, trends and perspectives in ontology alignment.

The contributions of this paper are classifications of ontology mapping approaches according various dimensions, comprehensive survey of latest approaches and discussion of the main trends and challenges in the ontology matching field.

The paper is discusses earlier (before 2008) research and classifications on ontology mapping and then discusses the newest ontology alignment approaches (proposed in 2007, 2008 or 2009). Next, the mapping evaluation approaches are analyzed, followed by a comparison of the mapping tools. Finally, the article is concluded.


The Main Characteristics Of Ontology Matching Approaches And Algorithms

There are many independent dimensions along which approaches, algorithms or tools can be examined, classified or selected. After analyzing above mentioned surveys and many other published in the last few year materials we propose for discussing the following dimensions for comprehensive description and classification of ontology matching approaches, algorithms and tools:

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