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

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.

[1]  Amit P. Sheth,et al.  SwetoDblp ontology of Computer Science publications , 2007, J. Web Semant..

[2]  Yi Zeng,et al.  On Granular Knowledge Structures , 2008, ArXiv.

[3]  S. Tsumoto,et al.  Rough Set Theory and Granular Computing , 2003 .

[4]  Yiyu Yao,et al.  A Logic Approach to Granular Computing , 2008, Int. J. Cogn. Informatics Nat. Intell..

[5]  Yiyu Yao,et al.  A Unified Framework of Granular Computing , 2008 .

[6]  Mark Fischetti,et al.  Weaving the web - the original design and ultimate destiny of the World Wide Web by its inventor , 1999 .

[7]  Dieter Fensel,et al.  Unifying Reasoning and Search to Web Scale , 2007, IEEE Internet Computing.

[8]  C. V. Ramamoorthy,et al.  Anytime reasoning in first-order logic , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[9]  Mohand Boughanem,et al.  Towards a graph-based user profile modeling for a session-based personalized search , 2009, Knowledge and Information Systems.

[10]  Yan Lin,et al.  Researches on granular reasoning based on granular space , 2008, GrC 2008.

[11]  Bo Zhang,et al.  Theory and Applications of Problem Solving , 1992 .

[12]  Alexander Pretschner,et al.  Ontology based personalized search , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[13]  John R. Anderson,et al.  Reflections of the Environment in Memory Form of the Memory Functions , 2022 .

[14]  Stefano Ceri.  Search Computing , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[15]  Mohand Boughanem,et al.  Evaluation of contextual information retrieval effectiveness: overview of issues and research , 2010, Knowledge and Information Systems.

[16]  Sanjiv K. Bhatia,et al.  Selection of search terms based on user profile , 1992, SAC '92.

[17]  桐山 伸也.  "The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind," Marvin Minsky, Simon & Schuster, 2006(私のすすめるこの一冊,コーヒーブレイク) , 2007 .

[18]  Germano Resconi,et al.  Granular reasoning using zooming in & out. Part 1. Propositional reasoning (an extended abstract) , 2003 .

[19]  Mario Cannataro,et al.  The knowledge grid , 2003, CACM.

[20]  Yiyu Yao,et al.  Perspectives of granular computing , 2005, 2005 IEEE International Conference on Granular Computing.

[21]  Frank van Harmelen,et al.  Reasoning with Inconsistent Ontologies , 2005, IJCAI.

[22]  Dieter Fensel,et al.  Towards LarKC: A Platform for Web-Scale Reasoning , 2008, 2008 IEEE International Conference on Semantic Computing.

[23]  Albert-László Barabási,et al.  Linked: The New Science of Networks , 2002 .

[24]  Qing Liu,et al.  Granular Logic with Closeness Relation "~lambda" and Its Reasoning , 2005, RSFDGrC.

[25]  Qing Liu,et al.  Researches on granular reasoning based on granular space , 2008, 2008 IEEE International Conference on Granular Computing.

[26]  Mamede Lima-Marques,et al.  Contextual Negations and Reasoning with Contradictions , 1991, IJCAI.

[27]  Frank van Harmelen,et al.  A semantic web primer , 2004 .

[28]  M. Ross Quillian,et al.  Retrieval time from semantic memory , 1969 .

[29]  Mario F. Triola.  Elementary Statistics Using the Graphing Calculator: For the TI-83/84 Plus , 2004 .

[30]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[31]  Ryszard S. Michalski,et al.  Variable Precision Logic , 1986, Artif. Intell..

[32]  Yiyu Yao,et al.  DBLP-SSE: A DBLP Search Support Engine , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[33]  Germano Resconi,et al.  Granular Reasoning Using Zooming In & Out , 2003, RSFDGrC.

[34]  T. Rogers,et al.  Object categorization: reversals and explanations of the basic-level advantage. , 2007, Journal of experimental psychology. General.

[35]  Jerome L. Myers,et al.  Research Design and Statistical Analysis , 1991 .

[36]  Xiangji Huang,et al.  Integrating multiple document features in language models for expert finding , 2010, Knowledge and Information Systems.

[37]  Ivan Koychev,et al.  Gradual Forgetting for Adaptation to Concept Drift , 2000 .

[38]  Yiyu Yao,et al.  The Art of Granular Computing , 2007, RSEISP.

[39]  Tsau Young Lin,et al.  Granular Computing , 2003, RSFDGrC.

[40]  Geoffrey R. Loftus,et al.  Evaluating forgetting curves. , 1985 .

[41]  Yan Wang,et al.  Unifying Web-Scale Search and Reasoning from the Viewpoint of Granularity , 2009, AMT.

[42]  Edward J. Wisniewski,et al.  Superordinate and basic category names in discourse: A textual analysis , 1989 .

[43]  M. Minsky.  The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind , 2006 .

[44]  Witold Pedrycz,et al.  Granular computing: an introduction , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).