Partially constructed knowledge for semantic query
Among the developments of information technology, the most popular tools nowadays for seeking the knowledge are the Google or Yahoo keywords-based search engines on the Internet. Users can easily obtain the information they need, but they still have to read and organize those documents by themselves...
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Veröffentlicht in: | Expert systems with applications 2009-08, Vol.36 (6), p.10168-10179 |
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container_title | Expert systems with applications |
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creator | Wang, Tzone I. Hsieh, Tung Cheng Tsai, Kun Hua Chiu, Ti Kai Lee, Ming Che |
description | Among the developments of information technology, the most popular tools nowadays for seeking the knowledge are the Google or Yahoo keywords-based search engines on the Internet. Users can easily obtain the information they need, but they still have to read and organize those documents by themselves. Due to that reason, users have to spend most of time in browsing and skipping the documents they have searched. In order to facilitate this process, this paper proposes a query-based ontology knowledge acquisition system which dynamically constructs query-based partial ontology to provide proficient answers for users’ queries. To construct the relationships and hierarchy of concepts in such an ontology, the formal concept analysis approach is adopted. After the ontology is built, the system can deduct the specific answer according to the relationships and hierarchy of ontology without asking users to read the whole document sets. We collected three kinds of sports news pages as source documents including those regarding NBA, CPBL and MLB to evaluate the precision of the system function in the experiment, which, as a result, reveals that the proposed approach indeed can work effectively. |
doi_str_mv | 10.1016/j.eswa.2008.12.030 |
format | Article |
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issn | 0957-4174 1873-6793 |
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source | Elsevier ScienceDirect Journals |
subjects | Formal concept analysis Knowledge acquisition Ontology Search engine |
title | Partially constructed knowledge for semantic query |
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