Paper Info
Title
An Integrated Solution for Improving Semantic Content Searching in Distributed Environment
Abstract
The research in the ontology-based information retrieval has made a significant progress recently, especially in the Web domain. In this kind of retrieval system, the domain ontology is used as the backbone of the searching process. However, with the heterogeneity of data storing formats on the Web, many of ontology-driven systems development approach suffer from inconsistencies during mapping between ontologypsilas concepts and corresponding data from data sources. Thus, sophisticated data integration and mining tools are required so domain scientists can more easily retrieve answers to their queries. To address these requirements, we have proposed a Web content query refinement framework integrating both ontology and Web mining techniques to assist the material domain scientists. We used the Web ontology language (OWL) to represent structured knowledge about materials. We have used SWRL, the semantic Web rule language, as a high-level query language that uses the implemented OWL based mappings method. The main merit of our proposed solution is that the user can interactively process (accept or reject) the knowledge gathered during the mining process to enrich the ontology base (ontology learning), which leads to better query precision. We present an evaluation study using Query&Navi, to show the benefit of the proposed approach.
Year
DOI
Venue
2009
10.1109/GCC.2009.15
GCC
Keywords
Field
DocType
ontology,domain scientist,swrl,integrated solution,web content query refinement,data source,systems development approach,semantic content searching,query refinement,semantic web rule language,web mining,web content query refinement framework,user interfaces,learning (artificial intelligence),domain ontology,improving semantic content searching,information retrieval,query languages,corresponding data,high-level query language,ontology base,web ontology language,search engine,ontotwo,web content mining,owl,query&navi,user interaction,knowledge representation languages,semantic web,ontologies (artificial intelligence),query answering,data mining,data storage format,ontology learning,search engines,data integration,query formulation,web mining technique,distributed environment,query language,clustering algorithms,data integrity,learning artificial intelligence,prototypes,ontologies,lattices
Ontology (information science),Ontology-based data integration,Ontology alignment,Process ontology,Information retrieval,Computer science,Ontology Inference Layer,OWL-S,Upper ontology,Ontology learning
Conference
ISBN
Citations 
PageRank 
978-0-7695-3766-5
0
0.34
References 
Authors
14
2
Name
Order
Citations
PageRank
Yacouba Goita100.34
Changjun Hu213027.56