TY - GEN
T1 - Towards context-sensitive domain ontology extraction
AU - Lau, Raymond Y. K.
AU - Hao, Jin Xing
AU - Tang, Maolin
AU - Zhou, Xujuan
PY - 2007
Y1 - 2007
N2 - 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. © 2007 IEEE.
AB - 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. © 2007 IEEE.
KW - Domain ontology
KW - Information retrieval
KW - Ontology extraction
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=39749175562&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-39749175562&origin=recordpage
U2 - 10.1109/HICSS.2007.570
DO - 10.1109/HICSS.2007.570
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769527558
SN - 9780769527550
BT - Proceedings of the Annual Hawaii International Conference on System Sciences
T2 - 40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07
Y2 - 3 January 2007 through 6 January 2007
ER -