Text Semantic Mining Model Based on the Algebra of Human Concept Learning

Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model TSMM based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

[1]  Yingxu Wang.  Neuroinformatics Models of Human Memory: Mapping the Cognitive Functions of Memory onto Neurophysiological Structures of the Brain , 2013, Int. J. Cogn. Informatics Nat. Intell..

[2]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3]  Du Zhang.  A Fix-Point Semantics for Rule-Base Anomalies , 2007, Int. J. Cogn. Informatics Nat. Intell..

[4]  Yingxu Wang.  Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (Advances in Cognitive Informatics and Natural Intelligence (Acini) Book Series) , 2009 .

[5]  Paul Barford,et al.  A Machine Learning Approach to TCP Throughput Prediction , 2010, IEEE/ACM Trans. Netw..

[6]  John D. Lafferty,et al.  Correlated Topic Models , 2005, NIPS.

[7]  Jean Sallantin,et al.  Humans and Machines: Nature of Learning and Learning of Nature , 2010 .

[8]  Phil Turner,et al.  Exploration of Space, Technology, and Spatiality: Interdisciplinary Perspectives , 2008 .

[9]  Hong-Gee Kim,et al.  Exploiting noun phrases and semantic relationships for text document clustering , 2009, Inf. Sci..

[10]  David S. Walker,et al.  A Cognitive Machine in the Organization of Study , 2009 .

[11]  Witold Kinsner,et al.  A unified approach to fractal dimensions , 2005, Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005)..

[12]  Phyllis Chiasson,et al.  Relational Thinking Styles and Natural Intelligence: Assessing Inference Patterns for Computational Modeling , 2012 .

[13]  Yingxu Wang,et al.  The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain , 2007, Int. J. Cogn. Informatics Nat. Intell..

[14]  Zhongzhi Shi,et al.  A Novel Plausible Model for Visual Perception , 2008, Int. J. Cogn. Informatics Nat. Intell..

[15]  Yingxu Wang.  Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing and Machine Reasoning (I) , 2011, Int. J. Cogn. Informatics Nat. Intell..

[16]  Ka-Lok Ng,et al.  Scaling Behavior of Maximal Repeat Distributions in Genomic Sequences: A Randomize Test Follow Up Study , 2010 .

[17]  Andrew McCallum,et al.  The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email , 2005 .

[18]  Fangfang Liu,et al.  Discovery of associated topics for the intelligent browsing , 2008, 2008 First IEEE International Conference on Ubi-Media Computing.

[19]  Yingxu Wang.  On Cognitive Informatics , 2003 .

[20]  Gustavo Abib,et al.  Organizational and technological implications of cognitive machines: designing future information management systems , 2011 .

[21]  J. Feldman.  An algebra of human concept learning , 2006 .

[22]  Xue Chen,et al.  Building Association Link Network for Semantic Link on Web Resources , 2011, IEEE Transactions on Automation Science and Engineering.

[23]  Yingxu Wang,et al.  On Concept Algebra and Knowledge Representation , 2006, 2006 5th IEEE International Conference on Cognitive Informatics.

[24]  André Mayers,et al.  AURELLIO: A Cognitive Computational Knowledge Representation Theory , 2007, Int. J. Cogn. Informatics Nat. Intell..

[25]  Yingxu Wang,et al.  Formal Modeling and Specification of Design Patterns Using RTPA , 2008, Int. J. Cogn. Informatics Nat. Intell..

[26]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[27]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[28]  Jeff Heflin,et al.  SHOE: A Knowledge Representation Language for Internet Applications , 1999 .

[29]  Sean Bechhofer,et al.  OWL: Web Ontology Language , 2009, Encyclopedia of Database Systems.

[30]  Yingxu Wang,et al.  The Theoretical Framework of Cognitive Informatics , 2007, Int. J. Cogn. Informatics Nat. Intell..

[31]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[32]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[33]  Hai Zhuge,et al.  Schema Theory for Semantic Link Network , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.

[34]  Mitsuru Ishizuka.  Textual affect sensing and affective communication , 2012, 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing.

[35]  Witold Pedrycz,et al.  Cognitive Informatics and Cognitive Computing in Year 10 and Beyond , 2011, Int. J. Cogn. Informatics Nat. Intell..

[36]  Xiangfeng Luo,et al.  Measuring Textual Context Based on Cognitive Principles , 2009, Int. J. Softw. Sci. Comput. Intell..

[37]  Mercedes Hidalgo-Herrero,et al.  Knowledge Adquisition in a Cooperative and Competitive Framework , 2011 .

[38]  Witold Kinsner.  Is entropy suitable to characterize data and signals for cognitive informatics? , 2004, Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004..

[39]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[40]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[41]  Reena Garg,et al.  A Novel Probabilistic Approach for Efficient Information Retrieval , 2010 .