@article{75d0ad4ba8a64d0c965b2a5938e6fb62,
  title     = "User-centric Query Refinement and Processing Using Granularity Based Strategies",
  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. © 2010 Springer-Verlag London Limited.",
  author    = "Y. Zeng and N. Zhong and Y. Wang and Y. Qin and Z. Huang and H Zhou and Y Yao and {van Harmelen}, F.A.H.",
  year      = "2011",
  doi       = "10.1007/s10115-010-0298-8",
  volume    = "27",
  pages     = "419--450",
  journal   = "Knowledge and Information Systems",
  issn      = "0219-1377",
  publisher = "Springer London",
  number    = "3",
}

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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.","author":[{"firstnames":["Y."],"propositions":[],"lastnames":["Zeng"],"suffixes":[]},{"firstnames":["N."],"propositions":[],"lastnames":["Zhong"],"suffixes":[]},{"firstnames":["Y."],"propositions":[],"lastnames":["Wang"],"suffixes":[]},{"firstnames":["Y."],"propositions":[],"lastnames":["Qin"],"suffixes":[]},{"firstnames":["Z."],"propositions":[],"lastnames":["Huang"],"suffixes":[]},{"firstnames":["H"],"propositions":[],"lastnames":["Zhou"],"suffixes":[]},{"firstnames":["Y"],"propositions":[],"lastnames":["Yao"],"suffixes":[]},{"propositions":["van Harmelen"],"lastnames":[],"firstnames":["F.A.H."],"suffixes":[]}],"year":"2011","doi":"10.1007/s10115-010-0298-8","volume":"27","pages":"419–450","journal":"Knowledge and Information Systems","issn":"0219-1377","publisher":"Springer London","number":"3","bibtex":"@article{75d0ad4ba8a64d0c965b2a5938e6fb62,\n title = \"User-centric Query Refinement and Processing Using Granularity Based Strategies\",\n 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. © 2010 Springer-Verlag London Limited.\",\n author = \"Y. Zeng and N. Zhong and Y. Wang and Y. Qin and Z. 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