Towards Mutual Understanding Among Ontologies - Rule-Based and Learning-Based Matching Algorithms for Ontologies

ISBN-13: 9783639115567 / Angielski / Miękka / 2008 / 132 str.

Jingshan Huang

Opis

Ontologies are formal, declarative knowledge representation models, forming a semantic foundation for many domains. As the Semantic Web gains attention as the next generation of the Web, ontologies importance increases accordingly. Different ontologies are hetero­geneous, which can lead to misunderstandings, so there is a need for them to be related. The suggested approaches can be categorized as either rule-based or learning-based. The former works on ontology schemas, and the latter considers both schemas and instances. This book makes 6 assumptions to bound the matching problem, then presents 3 systems towards the mutual reconciliation of concepts from different ontologies: (1) the Puzzle system belongs to the rule-based approach; (2) the SOCCER (Similar Ontology Concept ClustERing) system is mostly a learning-based solution, integrated with some rule-based techniques; and (3) the Compatibility Vector system, although not an ontology-matching algorithm by itself, instead is a means of measuring and maintaining ontology compatibility, which helps in the mutual understanding of ontologies and determines the compatibility of services (or agents) associated with these ontologies.

O autorze

Dr. Jingshan Huang is an Assistant Professor in Computer Science
at University of South Alabama. He has conducted many research
funded by DoD and NIH, and his research concentrates in machine
intelligence and semantic integration. He is the author of over
20 technical papers and has served as a PC member in many
international conferences/journals.