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

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.