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Top1. Introduction
As the number of ontologies available increases, the need to align these ontologies increases (Kalfoglou & Schorlemmer, 2003). A single ontology is not sufficient to support the distributed nature of the semantic web or the operations of many businesses (Thomas, Redmond, & Yoon, 2009) or governments (Santos & Madeira, 2010). In order to effectively leverage knowledge represented in these overlapping ontologies, ontology alignment may be useful. Ontology alignment is achieved in part by mapping the concepts of one ontology to the concepts of another ontology. Ontology alignment may also be a step in the larger process of ontology integration or merging (Pinto, Gomez-Perez, & Martins, 1999).
Ontologies may be manually mapped by a knowledge engineer or domain expert; however, this process has shortcomings. Manual mapping is tedious, error prone, and hinders ontology maintenance (Ding & Foo, 2002). Automated mapping may occur at the schema-level or the instance-level. Schema-level mapping begins with two schemas as input producing output in the form of semantic links between the elements of the input schemas (Rahm & Bernstein, 2001). These links may range from simple one-to-one links to complex many-to-many, semantically labeled links (Embley, Xu, & Ding, 2004). Instance-level mapping occurs by using classified instance data in order to construct links between concepts based on the co-occurrences of the instances (Isaac, Van der Meij, Schlobach, & Wang, 2007). Finally, a hybrid approach employs schema and instance techniques.
This paper presents a hybrid, ontology alignment system, called ALIGN. Two crucial steps in this system are:
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A schema-based method to determine the similarity between a concept in one ontology and a concept in another ontology based on the lexical match of the two. This is considered a schema-level method in that it employs the concept category of each ontology, although it does not make inferences based on larger structures in either ontology.
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An instance-based approach to determine the similarity between two concepts based on their instances using a Jaccard coefficient similarity measure.
Notably, this work illustrates how several, readily available Semantic Web technologies can be used for ontology alignment.
The system that is developed could be applied to any two ontologies that maintain the class-instance relationship. To demonstrate the system, the authors have constructed two drug adverse reaction ontologies. A set of tests were then done on the extent to which the system could successfully exploit the information in both ontologies through mapping. The following sections present related work, the system architecture, a detailed development methodology and an application, and the conclusions.
TopProminent ontology alignment systems include GLUE, ONIONS, FCA-Merge, and S-Match. Each uses hybrid mapping and employs a wide range of techniques (Lambrix & Tan, 2006) to include:
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Linguistic matching that uses similar words or hyponyms to match concepts.
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Structure-matching that exploits the taxonomic structure of the ontologies.
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Constraint-based techniques that use domain knowledge outside the ontologies to guide or constrain matching.
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Instance-based matching that utilizes the patterns of matches among the instances of a class to decide whether or not the classes match.
Knowledge from outside the ontologies being aligned is typically employed. One of the most popular generic sources is Word Net.