Towards Mutual Understanding Among Ontologies - Rule-Based and Learning-Based Matching Algorithms for Ontologies (Paperback) (ISBN-13: 9783639115567)

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 hetero-geneous, 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.

Author: Jingshan Huang
ISBN-10: 3639115562
ISBN-13: 9783639115567
Publisher: VDM Verlag
Language: English
Published: 12/30/2008
Pages: 132
Format: Paperback
Weight: 0.41lbs
Size: 9.00h x 6.00w x 0.28d