Granularity-Based User-Centric Multi-Strategies and Application in Knowledge Retrieval

Granular computing is a general computing paradigm of problem solving for effectively using granules in problem solving. From the viewpoint of granularity, this paper presents a new granular computing data cycle model in which i nformation granule construction, granular space, information granule operations, and knowledge acquisition approach are proposed respectively. To do a more user oriented way, a multi-strategies model, namely, Base-Level learning strategy, user interest retention strategy, and starting point strategy, introduces to unify search and reasoning for effective problem solving . Then, we discuss granularity-based unification using three strateg ies mentioned above and show three-levels of granularity from human problem solving to a wide variety of user needs satisf ied . Furthermore, on the basis of user -centric multi-strategies and granular information processing, we develop a conceptual framework of granularity-based knowledge retrieval model, which enlarges the application areas of granular computing.

[1]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[2]  Giovanna Castellano,et al.  Information granulation via neural network-based learning , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[3]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[4]  Otto H. MacLin,et al.  Cognitive psychology, 7th ed. , 2005 .

[5]  Yiyu Yao,et al.  Knowledge Retrieval (KR) , 2007 .

[6]  Tsau Young Lin,et al.  Granular computing: Models and applications , 2010 .

[7]  Richi Nayak,et al.  A knowledge retrieval model using ontology mining and user profiling , 2008, Integr. Comput. Aided Eng..

[8]  Yiyu Yao,et al.  User-centric query refinement and processing using granularity-based strategies , 2010, Knowledge and Information Systems.

[9]  L. Zadeh A new direction in AI: toward a computational theory of perceptions , 2002 .

[10]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[11]  Chun-Chin Hsu,et al.  An information granulation based data mining approach for classifying imbalanced data , 2008, Inf. Sci..

[12]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU) - an outline , 2005, GrC.

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

[14]  Hermann Ebbinghaus (1885) Memory: A Contribution to Experimental Psychology , 2013, Annals of Neurosciences.

[15]  Yan Lin,et al.  Granular Reasoning and Decision System’s Decomposition , 2012 .

[16]  Janusz Kacprzyk,et al.  Advances in Web Intelligence , 2003, Lecture Notes in Computer Science.

[17]  Tang Shiwei,et al.  A Spatial Feature Selection Method Based on Maximum Entropy Theory , 2003 .

[18]  Abraham Kandel,et al.  Advances in Web Intelligence and Data Mining , 2006, Studies in Computational Intelligence.

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

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

[21]  Lin Sun,et al.  Granular Space-Based Feature Selection and Its Applications , 2013, J. Softw..

[22]  Yiyu Yao,et al.  Granular Computing as a Basis for Consistent Classification Problems , 2002 .

[23]  Andrzej Bargiela,et al.  Recursive information granulation: aggregation and interpretation issues , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Lin Sun,et al.  Granular Computing-based Granular Structure Model and its Application in Knowledge Retrieval , 2012 .

[25]  John R. Anderson,et al.  The dynamics of scaling: a memory-based anchor model of category rating and absolute identification. , 2005, Psychological review.

[26]  Yiyu Yao,et al.  Granular Structures and Approximations in Rough Sets and Knowledge Spaces , 2009 .

[27]  Jianhua Ma,et al.  Research challenges and perspectives on Wisdom Web of Things (W2T) , 2010, The Journal of Supercomputing.

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

[29]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Song Guo A Spatial Feature Selection Method Based on Maximum Entropy Theory , 2003 .

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

[32]  Darryl W. Schneider,et al.  Modeling fan effects on the time course of associative recognition , 2012, Cognitive Psychology.

[33]  E. Amine Lehtihet,et al.  A classification algorithm and optimal feature selection methodology for automated solder joint defect inspection , 1998 .

[34]  Yuehwern Yih,et al.  Knowledge acquisition through information granulation for imbalanced data , 2006, Expert Syst. Appl..

[35]  Tsau Young Lin,et al.  Granular Computing and Modeling the Human Thoughts in Web Documents , 2007, IFSA.

[36]  Long-Sheng Chen,et al.  A neural network based information granulation approach to shorten the cellular phone test process , 2006, Comput. Ind..

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

[38]  F. A. Grootjen,et al.  Conceptual query expansion , 2006, Data Knowl. Eng..

[39]  Lin Sun,et al.  Granularity Partition-based Feature Selection and Its Application in Decision Systems , 2012 .