Journal
CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Volume -, Issue -, Pages 1727-1736Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3132847.3132969
Keywords
spatial database; spatial keyword query; interactive query
Funding
- National Natural Science Foundation of China [61402312, 61502324, 61572335, 61472263]
- Natural Science Foundation of Jiangsu Province of China [BK20151223]
- Australian Research Council [DP160102412, DP170104747]
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Conventional spatial keyword queries confront the difficulty of returning desired objects that are synonyms but morphologically different to query keywords. To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics. It aims to enhance the conventional queries by not only making sense of the query keywords, but also refining the understanding of query semantics through interactions. On top of the probabilistic topic model, a novel interactive strategy is proposed to precisely infer the latent query semantics by learning from user feedbacks. In each interaction, the returned objects are carefully selected to ensure effective inference of user intended query semantics. Query processing is carried out on a small candidate object set at each round of interaction, and the whole querying process terminates when the latent query semantics learned from user feedback becomes explicit enough. The experimental results on real check-in dataset demonstrates that the quality of results has been significantly improved through limited number of interactions.
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