combiSQORE: A Combinative-Ontology Retrieval System for Next Generation Semantic Web Applications

Abstract

SUMMARY In order to timely response to a user query at run-time, next generation Semantic Web applications demand a robust mechanism to dynamically select one or more existing ontologies available on the Web and combine them automatically if needed. Although existing ontology retrieval systems return a lengthy list of resultant ontologies, they cannot identify which ones can completely meet the query requirements nor determine a minimum set of resultant ontologies that can jointly satisfy the requirements if no single ontology is available to satisfy them. Therefore, this paper presents an ontology retrieval system, namely combiSQORE, which can return single or combinative ontologies that completely satisfy a submitted query when the available ontology database is adequate to answer such query. In addition, the proposed system ranks the returned results based on their semantic similarities to the given query and their modification (integration) costs. The experimental results show that combiSQORE system yields practical combinative ontologies and useful rankings.

Enhancing Folksonomy-Based Content Retrieval with Semantic Web Technology

“…To support reasoning capabilities and enhance the matching results, SQORE uses an ontology reasoner and employs also a semantic lexical database in the query evaluation process. As an example, SQORE could yield an ontology defining the concept infant as a relevant ontology to the given query term baby; • combiSQORE (Ungrangsi, Anutariya, & Wuwongse, 2008)-a greedy algorithm that determines the most relevant and minimal (irreducible) sets of ontologies that completely satisfy the query; and • OMEGA (Ungrangsi & Simperl, 2008)an algorithm that automatically generates ontology metadata of a given ontology by using data-mining techniques and by referring to trustworthy ontology metadata libraries. Its metadata elements are useful for evaluating the ontology in various aspects and for computing ranking measures.…”

“…Hence, for m: the number of the given query terms and n: the size of an ontology database, the system's computation complexity is O(mn 2 log n), which is O(n 2 log n), when m << n. The detailed explanation of the complexity analysis can be found in Ungrangsi, Anutariya, and Wuwongse (2008).…”