User-centric query refinement and processing using granularity-based strategies

  • Authors:

  • Yi Zeng;Ning Zhong;Yan Wang;Yulin Qin;Zhisheng Huang;Haiyan Zhou;Yiyu Yao;Frank van Harmelen

  • Affiliations:

  • Beijing University of Technology, International WIC Institute, 100124, Beijing, China;Beijing University of Technology, International WIC Institute, 100124, Beijing, China and Maebashi Institute of Technology, Department of Life Science and Informatics, 371-0816, Maebashi, Japan;Beijing University of Technology, International WIC Institute, 100124, Beijing, China;Beijing University of Technology, International WIC Institute, 100124, Beijing, China and Carnegie Mellon University, Department of Psychology, 15213, Pittsburgh, PA, USA;Vrije Universiteit Amsterdam, Department of Computer Science, 1081 HV, Amsterdam, The Netherlands;Beijing University of Technology, International WIC Institute, 100124, Beijing, China;Beijing University of Technology, International WIC Institute, 100124, Beijing, China and University of Regina, Department of Computer Science, S4S 0A2, Regina, SK, Canada;Vrije Universiteit Amsterdam, Department of Computer Science, 1081 HV, Amsterdam, The Netherlands

  • Venue:
  • Knowledge and Information Systems
  • Year:
  • 2011

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Abstract

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.