Semantic Technologies for Searching in e-Science Grids

Searching is a key function in scientific cyber-infrastructures; there these systems need to implement superior meaning-based search functionalities powered by suitable semantic technologies. These required semantic technologies should enable computers to comprehend meaning of the objects being searched and user’s search intentions, compare these meanings, and discern which object may satisfy user’s need. We present a survey of meaning representation and comparison technologies, followed by a design of meaning representation and comparison technique which is coherent to the cognitive science and linguistics models. This proposed design addresses the key requirement of meaning compositionality which has not been addressed adequately and efficiently by existing research. We present an algebraic theory and techniques to represent hierarchically composed concepts as a tensor which is amenable to efficient semantic similarity computation. We delineate a data structure for the semantic descriptors/keys and an algorithm to generate them and describe an algorithm to compute the semantic similarity of two given descriptors (tensors). This meaning comparison technique discerns complex meaning while enabling search query relaxation and search key interchangeability. This is achieved without the need of a meaning knowledgebase (ontology).

[1]  Rochelle Lieber,et al.  The Oxford handbook of compounding , 2011 .

[2]  Simon D. Levy,et al.  Compositional Connectionism in Cognitive Science , 2004 .

[3]  Mirella Lapata,et al.  Vector-based Models of Semantic Composition , 2008, ACL.

[4]  Filippo Menczer,et al.  Algorithmic detection of semantic similarity , 2005, WWW '05.

[5]  Martin Bergman,et al.  The deep web:surfacing the hidden value , 2000 .

[6]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[7]  Rohana K. Rajapakse,et al.  Fast Access to Concepts in Concept Lattices via Bidirectional Associative Memories , 2005, Neural Computation.

[8]  John Darlington,et al.  A Semantic Similarity Measure for Semantic Web Services , 2005 .

[9]  Wayne D. Gray,et al.  BLOSSOM: Best Path Length on a Semantic Self-Organizing Map , 2008 .

[10]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[11]  Michael K. Bergman.  White Paper: The Deep Web: Surfacing Hidden Value , 2001 .

[12]  Gina R. Kuperberg,et al.  Neural mechanisms of language comprehension: Challenges to syntax , 2007, Brain Research.

[13]  Gregory L. Murphy,et al.  Comprehending Complex Concepts , 1988, Cogn. Sci..

[14]  Y. Grodzinsky.  The neurology of syntax: Language use without Broca's area , 2000, Behavioral and Brain Sciences.

[15]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

[16]  Monika Ekiert,et al.  The Bilingual Brain , 2003 .

[17]  E. Zurif,et al.  Syntactic and Semantic Composition , 2000, Brain and Language.

[18]  Dominic Widdows,et al.  Geometric Ordering of Concepts, Logical Disjunction, and Learning by Induction , 2004, AAAI Technical Report.

[19]  Amitava Biswas,et al.  Search Co-Ordination by Semantic Routed Network , 2009, 2009 Proceedings of 18th International Conference on Computer Communications and Networks.

[20]  Ling Wei,et al.  Composition of concept lattices , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[21]  John R. Anderson.  A spreading activation theory of memory. , 1983 .

[22]  Nunzio Bottini,et al.  A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes , 2004, Nature Genetics.

[23]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[24]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

[25]  Dominic Widdows,et al.  Using LSA and Noun Coordination Information to Improve the Recall and Precision of Automatic Hyponymy Extraction , 2003, CoNLL.

[26]  David M. W. Powers,et al.  Measuring Semantic Similarity in the Taxonomy of WordNet , 2005, ACSC.

[27]  Benoît Lemaire,et al.  Incremental Construction of an Associative Network from a Corpus , 2004 .

[28]  Howard S. Kirshner.  Language Studies in the Third Millennium , 2000, Brain and Language.

[29]  P. Hagoort.  On Broca, brain, and binding: a new framework , 2005, Trends in Cognitive Sciences.

[30]  R. Berndt,et al.  Semantic and syntactic processes in aphasia: a review of the literature. , 1978, Psychological bulletin.

[31]  P. Pirolli,et al.  Spread of activation. , 1984 .

[32]  Roy Rada,et al.  Development and application of a metric on semantic nets , 1989, IEEE Trans. Syst. Man Cybern..

[33]  E. Saffran.  The Organization of Semantic Memory: In Support of a Distributed Model , 2000, Brain and Language.

[34]  Dominic Widdows,et al.  Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination , 2004 .

[35]  Nicola Fanizzi,et al.  A Semantic Similarity Measure for Expressive Description Logics , 2009, ArXiv.

[36]  Peter W. Culicover,et al.  The simpler syntax hypothesis , 2006, Trends in Cognitive Sciences.

[37]  Krzysztof Janowicz,et al.  Sim-DL: Towards a Semantic Similarity Measurement Theory for the Description Logic ALCNR in Geographic Information Retrieval , 2006, OTM Workshops.

[38]  Alexander Borgida,et al.  Towards Measuring Similarity in Description Logics , 2005, Description Logics.

[39]  Dominic Widdows.  A Mathematical Model for Context and Word-Meaning , 2003, CONTEXT.

[40]  Dominic Widdows,et al.  Orthogonal Negation in Vector Spaces for Modelling Word-Meanings and Document Retrieval , 2003, ACL.

[41]  Z. Ye,et al.  Involvement of cognitive control in sentence comprehension: Evidence from ERPs , 2008, Brain Research.

[42]  Dominic Widdows,et al.  Semantic Vector Products: Some Initial Investigations , 2008 .

[43]  Ina Bornkessel-Schlesewsky,et al.  Semantic composition engenders an N400: evidence from Chinese compounds , 2008, Neuroreport.

[44]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[45]  D. Medin,et al.  The role of theories in conceptual coherence. , 1985, Psychological review.

[46]  Ana Laura Rodríguez Redondo,et al.  Aspects of cognitive linguistics and neurolinguistics: conceptual structure and category-specific semantic deficits = Aspectos de lingüística cognitiva y neurolingüística: estructura conceptual y déficit semánticos de , 2004 .

[47]  Amitava Biswas,et al.  Representation of Complex Concepts for Semantic Routed Network , 2009, ICDCN.

[48]  Steffen Staab,et al.  Remindin': semantic query routing in peer-to-peer networks based on social metaphors , 2004, WWW '04.

[49]  Geoffrey C. Bowker,et al.  Interoperability strategies for scientific cyberinfrastructure: Research and practice , 2005, ASIST.

[50]  Maria Mercedes Piñango.  Understanding the architecture of language: the possible role of neurology , 2006, Trends in Cognitive Sciences.

[51]  Dominic Widdows,et al.  Unsupervised methods for developing taxonomies by combining syntactic and statistical information , 2003, NAACL.

[52]  John A. Barnden,et al.  Semantic Networks , 1998, Encyclopedia of Social Network Analysis and Mining.

[53]  D Y von Cramon,et al.  Segregating semantic and syntactic aspects of processing in the human brain: an fMRI investigation of different word types. , 2000, Cerebral cortex.

[54]  Andrei Broder,et al.  Network Applications of Bloom Filters: A Survey , 2004, Internet Math..

[55]  Ray Jackendoff,et al.  Compounding in the Parallel Architecture and Conceptual Semantics , 2011 .

[56]  J. Hirsch,et al.  Distinct cortical areas associated with native and second languages , 1997, Nature.

[57]  Amitava Biswas,et al.  Optimization of Semantic Routing Table , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[58]  M. Kanehisa,et al.  A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. , 2000, Nucleic acids research.

[59]  Yu. I. Dimitrienko.  Tensors in Continuum Mechanics , 2002 .

[60]  Evgeniy Gabrilovich,et al.  Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis , 2007, IJCAI.

[61]  Rohana K. Rajapakse,et al.  Text retrieval with more realistic concept matching and reinforcement learning , 2006, Inf. Process. Manag..