Ontology Alignment Techniques

Ontology Alignment Techniques

Marcos Martínez Romero (University of A Coruña, Spain), José Manuel Vázquez Naya (University of A Coruña, Spain), Javier Pereira Loureiro (University of A Coruña, Spain) and Norberto Ezquerra (Georgia Institute of Technology, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch189
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Abstract

Sometimes the use of a single ontology is not sufficient to cover different vocabularies for the same domain, and it becomes necessary to use several ontologies in order to encompass the entire domain knowledge and its various representations. Disciplines where this occurs include medical science and biology, as well as many of its associated subfields such as genetics, epidemiology, etc. This may be due to a domain’s complexity, expansiveness, and/or different perspectives of the same domain on the part of different groups of users. In such cases, it is essential to find relationships that may exist between the elements of a specific domain’s different ontologies, a process known as ontology alignment. There are several methods for identifying the relationships or correspondences between elements associated with different ontologies, and collectively these methods are called ontology alignment techniques. Many of these techniques stem from other fields of study (e.g., matching techniques in discrete mathematics) while others have been specifically designed for this purpose. The key to successfully aligning ontologies is based on the appropriate selection and implementation of a set of those ontology alignment techniques best suited for a particular alignment problem. Ontology alignment is a complex, tedious, and time-consuming task, especially when working with ontologies of considerable size (containing, for instance, thousands of elements or more) and which have complex relationships between the elements (for example, a particular problem domain in medicine). Furthermore, the true potential of ontology alignment is realized when different information-exchange processes are integrated automatically, thereby providing the framework for reaching a suitable level of efficient interoperability between heterogeneous systems. The importance of automatically aligning ontologies has therefore been a topic of major interest in recent years, and recently there has been a surge in a variety of software tools dedicated to aligning ontologies in either a fully or partially automated fashion. Some of these tools —generally referred to as ontology alignment systems— have been the result of well known and respected research centers, including Stanford University and Hewlett Packard Laboratories, for instance. In Shvaiko & Euzenat, 2007, updated information is given regarding the currently available ontology alignment systems. Each ontology alignment system combines different alignment approaches along with its own techniques, such that correspondences between the different ontologies can be detected in the most complete, precise, and efficient manner. Since each system is based on its own approximation techniques, different systems yield different results, and therefore the quality of the results can vary among systems. Most of the alignment systems are oriented to solving problems of a general nature, since ontologies associated with a single domain share certain characteristics that set them apart from ontologies associated with other domains. Recently, some systems have emerged that are designed to align ontologies in a specific domain. An example is the SAMBO alignment system (Lambrix, 2006) in the biomedical domain. These and other domain-specific systems can produce excellent results (when used for the domains for which they were designed), but are generally not useful when applied to other domains.
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Background

The key to ontology alignment is to find those entities in one ontology that may correspond to other entities in another ontology. Basically, this can be viewed as finding a similarity measure between elements (or so-called entities) associated with different ontologies, and subsequently selecting the set of correspondences that produce the strongest measures of similarity. There are, however, different ways to compute similarity measures; there are various studies dedicated to the classification of these techniques (Rahm & Bernstein, 2001, Euzenat & Valtchev, 2004, Euzenat et al., 2004, Shvaiko & Euzenat, 2005).

Key Terms in this Chapter

Ontology Alignment: A process that consists of finding the semantic relationships that may exist between different elements in different ontologies.

Ontology Alignment Technique: Method used to identify the semantic correspondences that may exist between the elements of different ontologies.

Thesaurus.: Networked collection of controlled vocabulary terms.

Ontology Alignment System: A software tool capable of conducting the alignment of ontologies in an automated fashion.

Ontology: A formal and explicit specification of a shared conceptualization.

Domain: Specific areas of interest (e.g., artworks by Picasso) or of knowledge (e.g., medicine, physics, etc.).

Ontology Entity: An ontology entity represents a conceptual element of the domain of discourse.

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