Towards Context-Sensitive Domain Ontology Extraction
Lau, Raymond Y.K., Hao, Jin-Xing, Tang, Maolin, & Zhou, Xujuan (2007) Towards Context-Sensitive Domain Ontology Extraction. In 40th Hawaii International Conference on System Sciences, January, 2007, Hawaii, The United States.
Although there has been a surge of interest in applying domain ontologies to facilitate communications among computers and human users, engineering of these ontologies turns out to be very labor intensive and time consuming. Recently, some learning methods have been proposed for automatic or semi-automatic extraction of ontologies. Nevertheless, the accuracy and computational efficiency of these methods should be improved to support large scale ontology extraction for real-world applications. This paper illustrates a novel domain ontology extraction method. In particular, contextual information of the knowledge sources is exploited for the extraction of high quality domain ontologies. By combining lexico-syntactic and statistical learning approaches, the accuracy and the computational efficiency of the extraction process can be improved. Empirical studies have confirmed that the proposed method can extract reliable domain ontology to improve the performance of information retrieval and facilitate human users to discover and refine domain ontology.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||information retrieval, learning (artificial intelligence), ontologies (artificial intelligence), statistical analysis|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||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.|
|Deposited On:||04 Mar 2008|
|Last Modified:||29 Feb 2012 23:35|
Repository Staff Only: item control page