User-centric Query Refinement and Processing Using Granularity Based Strategies

Y. Zeng, N. Zhong, Y. Wang, Y. Qin, Z. Huang, H Zhou, Y Yao, F.A.H. van Harmelen

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Under the context of large-scale scientific literatures, this paper provides a user-centric approach for refining and processing incomplete or vague query based on cognitive- and granularity-based strategies. From the viewpoints of user interests retention and granular information processing, we examine various strategies for user-centric unification of search and reasoning. Inspired by the basic level for human problem-solving in cognitive science, we refine a query based on retained user interests. We bring the multi-level, multi-perspective strategies from human problem-solving to large-scale search and reasoning. The power/exponential law-based interests retention modeling, network statistics-based data selection, and ontology-supervised hierarchical reasoning are developed to implement these strategies. As an illustration, we investigate some case studies based on a large-scale scientific literature dataset, DBLP. The experimental results show that the proposed strategies are potentially effective. © 2010 Springer-Verlag London Limited.
Original languageEnglish
Pages (from-to)419-450
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 2011


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