An ontology-based mining approach for user search intent discovery
Paper
Paper/Presentation Title | An ontology-based mining approach for user search intent discovery |
---|---|
Presentation Type | Paper |
Authors | Shen, Yan (Author), Li, Yuefeng (Author), Xu, Yue (Author), Iannella, Renato (Author), Algarni, Abdulmohsen (Author) and Tao, Xiaohui (Author) |
Editors | Cunningham, Sally Jo, Scholer, Falk and Thomas, Paul |
Journal or Proceedings Title | Proceedings of the 16th Australasian Document Computing Symposium (ADCS 2011) |
ERA Conference ID | 50001 |
Number of Pages | 8 |
Year | 2011 |
Place of Publication | Melbourne, Australia |
ISBN | 9781921426926 |
Web Address (URL) of Paper | http://www.cs.rmit.edu.au/adcs2011/pdf/paper16.pdf |
Conference/Event | 16th Australasian Document Computing Symposium (ADCS 2011) |
Australasian Document Computing Symposium | |
Event Details | 16th Australasian Document Computing Symposium (ADCS 2011) Event Date 02 Dec 2011 Event Location Canberra, Australia |
Event Details | Australasian Document Computing Symposium ADCS |
Abstract | Discovering proper search intents is a vital process to return desired results. It is constantly a hot research topic regarding information retrieval in recent years. Existing methods are mainly limited by utilizing context-based mining, query expansion, and user profiling techniques, which are still suffering from the issue of ambiguity in search queries. In this paper, we introduce a novel ontology-based approach in terms of a world knowledge base in order to construct personalized ontologies for identifying adequate concept levels for matching user search intents. An iterative mining algorithm is designed for evaluating potential intents level by level until meeting the best result. The propose-to-attempt approach is evaluated in a large volume RCV1 data set, and experimental results indicate a distinct improvement on top precision after compared with baseline models. |
Keywords | ontology mining; search intent; LCSH; world knowledge |
ANZSRC Field of Research 2020 | 460806. Human-computer interaction |
469999. Other information and computing sciences not elsewhere classified | |
460508. Information retrieval and web search | |
Public Notes | Copyright for this article remains with the authors. Permanent restricted access to published version due to publisher copyright policy. |
Byline Affiliations | Queensland University of Technology |
Department of Mathematics and Computing | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q100x/an-ontology-based-mining-approach-for-user-search-intent-discovery
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