Learning-based Relevance Feedback for Web-based Relation Completion
Li, Zhixu, Sitbon, Laurianne, & Zhou, Xiaofang (2011) Learning-based Relevance Feedback for Web-based Relation Completion. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management 2011, Association for Computing Machinery, Scotland, pp. 1535-1540.
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In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.
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|Item Type:||Conference Paper|
|Keywords:||Algorithms; Experimentation, Web-based Relation Completion; WebRC, Database Management, relevance feedback; retrieval models, Security; integrity|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Database Management (080604)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||03 Nov 2014 04:20|
|Last Modified:||01 Nov 2015 06:47|
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