Knowledge Acquisition for Semantic Search Systems
Wei, W, Barnaghi, PM and Bargiela, A (2008) Knowledge Acquisition for Semantic Search Systems In: ITSIM '08, 2008-08-26 - 2008-08-29, Kuala Lumpur, Malaysia.
["document_typename_application/force-download" not defined]
Available under License : See the attached licence file.
Semantic search extends the scope of conventional information search and retrieval paradigms from documentoriented and to entity and knowledge-centric search and retrieval. By attempting to provide direct and intuitive answers such systems alleviate information overload problem and reduce information seekers’ cognitive overhead. Ontologies and knowledge bases are fundamental cornerstones in semantic search systems based on which sophisticated search mechanisms and efficient search services are designed. Nevertheless, acquisition of quality knowledge from heterogeneous sources on the Web is never a trivial task. Transformation of data in existing databases seems a promising bootstrapping approach, while information providers may refuse to do so because of intellectual property issues. In this article we discuss issues related to knowledge acquisition for semantic search systems. In particular, we discuss ontology learning from unstructured text corpus, which is an automatic knowledge acquisition process using different techniques.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Depositing User:||Symplectic Elements|
|Date Deposited:||03 May 2012 12:54|
|Last Modified:||23 Sep 2013 19:23|
Actions (login required)
Downloads per month over past year