Abstract
In order to solve the problem of lack of effective methods for ontology inconsistency, a user preferences-oriented ontology alignment repair model is proposed. This model uses 0-1 linear programming method to minimize the remove cost; the structure and source of ontology are used to measure the axiom of importance; Finally, by choosing the minimal conflict sets and the user preferences limit repair strategy, the purpose of eliminating inconsistency, reducing semantic loss and guaranteeing credibility is achieved. The experimental results show that this method can effectively solve the problem of ontology inconsistency, and the restored ontology is more suitable for user preferences than traditional methods.
Highlights
Ontology provides important support for realizing knowledge sharing between semantic-oriented application systems
Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies
In this paper we used the Large BioMed Track, this track consists of finding alignments between the Foundational Model of Anatomy (FMA) and the National Cancer Institute Thesaurus (NCI), these ontologies are semantically rich and contain tens of thousands of classes [25], [26]
Summary
Ontology provides important support for realizing knowledge sharing between semantic-oriented application systems. To solve the problem of inconsistency in the ontology alignment, there are two methods [2]: use the ontology debugging and repair technology to find the conflict in the ontology and eliminate the contradiction knowledge through the revision of the conflict axiom; establish the conflicttolerated reasoning mechanism by defining non-classical semantics to avoid contradictions in classical reasoning and obtain meaningful conclusions. These two methods have different advantages in different application background. According to the information of ontology source, ontology structure, and user specific preferences, the ontology repair strategy is converted to 0-1 linear programming problem by maximizing the credibility of the ontology, minimizing the loss of information and fitting users’ preferences mostly as the goals, eliminating the conflict sets as constraints
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