Discovering semantic similarity association in semantic search system

  • Authors:

  • Shahdad Shariatmadari;Ali Mamat;Hamidah Ibahim;Norwati Mustapha

  • Affiliations:

  • Islamic Azad University -Shiraz branch, Shiraz, Iran;University Putra Malaysia, Serdang, Selangor, Malaysia;University Putra Malaysia, Serdang, Selangor, Malaysia;University Putra Malaysia, Serdang, Selangor, Malaysia

  • Venue:
  • Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
  • Year:
  • 2009

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Abstract

Discovering semantic similarity association among ontology instances is a challenging problem in semantic search systems. In a populated ontology there are numbers of different paths emanating from entities at instance level. Computing semantic similarity between these paths is an important issue in semantic analysis and semantic search applications. To answer some complex queries about the relatedness of two entities, we need to discover semantic similarity association between entities. Each entity has some relationships to the other entities which make a chain of classes and predicates in the RDF graph. Our main approach in this paper is to discover the similarity of two entities based on similarity of paths which are emanated from them. In order to calculate semantic similarity between entities, we calculate degree of semantic similarity between paths emanating from them. This paper takes into consideration the semantic similarity association between two entities and their similarity reflected in context. The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the application context. We will analyse the effects of applying different types of semantic similarity associations in discovering and ranking processes and figure out some directions that should be considered in designing the semantic search systems.