Context-Sensitive Ontology Matching in Electronic Business

Context-Sensitive Ontology Matching in Electronic Business

Jingshan Huang (University of South Alabama, USA) and Jiangbo Dang (Siemens Corporation, USA)
DOI: 10.4018/978-1-60960-485-1.ch012
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In today’s global economy, electronic business has offered great advantages to enhance the capabilities of traditional businesses. In order to satisfy the imposed requirement for businesses to coordinate with each other, electronic business partners are chosen to be represented by service agents. These agents need to understand each others’ service descriptions before successful coordination happens. Ontologies developed by service providers to describe their service can render help in this regard. Unfortunately, due to the heterogeneity implicit in independently designed ontologies, distributed agents are bound to face semantic mismatches and/or misunderstandings. This chapter introduces an innovative algorithm, Context-Sensitive Matching, to reconcile heterogeneous ontologies. This algorithm takes into consideration contextual information, via inference through a formal, robust statistical model based on confidence interval. In addition, an Artificial Neural Network is utilized to learning weights for different semantic aspects. At last, an agglomerative clustering algorithm is adopted to generate the final matching results.
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We give a brief review of the state-of-the-art ontology-matching techniques; in particular, we analyze the pros and cons of the existing two categories of matching algorithms: rule-based and learning-based algorithms. In addition, we also present an overview of current research in ontology and context, confidence interval applications, and ontology-based e-services.

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