Semantic approximate keyword query based on keyword and query coupling relationship analysis

Meng, X; Cao, L; Shao, J

Semantic approximate keyword query based on keyword and query coupling relationship analysis

Meng, X Cao, L

Shao, J

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Publication Type:: Conference Proceeding
Citation:: CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, 2014, pp. 529 - 538
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Meng, X	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Shao, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, 2014, pp. 529 - 538	en_US
dc.identifier.isbn	9781450325981	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121401
dc.description.abstract	Due to imprecise query intention, Web database users often use a limited number of keywords that are not directly related to their precise query to search information. Semantic approximate keyword query is challenging but helpful for specifying such query intent and providing more relevant answers. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query approach which generates semantic approximate answers by identifying a set of keyword queries from the query history whose semantics are related to the given keyword query. To capture the semantic relationships between keywords, a semantic coupling relationship analysis model is introduced to model both the intra- and inter - keyword couplings. Building on the coupling relationships between keywords, the semantic similarity of different keyword queries is then measured by a semantic matrix. The representative queries in query history are identified and then a priori order of remaining queries corresponding to each representative query in an off-line preprocessing step is created. These representative queries and associated orders are then used to expeditiously generate top-k ranked semantically related keyword queries. We demonstrate that our coupling relationship analysis model can accurately capture the semantic relationships both between keywords and queries. The efficiency of top-k keyword query selection algorithm is also demonstrated.	en_US
dc.relation.ispartof	CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management	en_US
dc.relation.isbasedon	10.1145/2661829.2661867	en_US
dc.title	Semantic approximate keyword query based on keyword and query coupling relationship analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Due to imprecise query intention, Web database users often use a limited number of keywords that are not directly related to their precise query to search information. Semantic approximate keyword query is challenging but helpful for specifying such query intent and providing more relevant answers. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query approach which generates semantic approximate answers by identifying a set of keyword queries from the query history whose semantics are related to the given keyword query. To capture the semantic relationships between keywords, a semantic coupling relationship analysis model is introduced to model both the intra- and inter - keyword couplings. Building on the coupling relationships between keywords, the semantic similarity of different keyword queries is then measured by a semantic matrix. The representative queries in query history are identified and then a priori order of remaining queries corresponding to each representative query in an off-line preprocessing step is created. These representative queries and associated orders are then used to expeditiously generate top-k ranked semantically related keyword queries. We demonstrate that our coupling relationship analysis model can accurately capture the semantic relationships both between keywords and queries. The efficiency of top-k keyword query selection algorithm is also demonstrated.

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http://hdl.handle.net/10453/121401