Skip to main content
SpringerLink
Log in

Semantic Technologies for Searching in e-Science Grids

  • Chapter
  • First Online:
Semantic e-Science

Part of the book series: Annals of Information Systems ((AOIS,volume 11))

  • 514 Accesses

Abstract

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).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. PubMed, http://www.ncbi.nlm.nih.gov/pubmed/. Accessed 1 Feb 2009

  2. SRS Server at EMBI-EBI: http://srs.ebi.ac.uk. Accessed 1 Feb 2009

  3. The SAO/NASA Astrophysics Data System: http://adswww.harvard.edu/. Accessed 1 Feb 2009

  4. NCAR Community Data Portal (CDP) http://cdp.ucar.edu/. Accessed 1 Feb 2009

  5. California Water CyberInfrastructure: http://bwc.lbl.gov/California/california.htm. Accessed 1 Feb 2009

  6. CUAHSI Hydrologic Information System (CUAHSI-HIS) http://his.cuahsi.org/. Accessed 1 Feb 2009

  7. Baker, K.S., Ribes, D., Millerand, F., Bowker, G.C.: Interoperability strategies for scientific cyberinfrastructure: Research and practice. Proceedings of the American Society for Information Science and Technology 42(1) (2005)

    Google Scholar 

  8. Bergman, M.K. White paper: The deep web: Surfacing hidden value. The Journal of Electronic Publishing 7(1) (2001)

    Google Scholar 

  9. Biswas, A., Mohan, S., Panigrahy, J., Tripathy, A., Mahapatra. R.: Enabling intention based search. Technical Report, Department of Computer Science, Texas A&M University (2008)

    Google Scholar 

  10. Medical Subject Headings, (MeSH): U.S. National Library of Medicine, http://www.nlm.nih.gov/mesh/. Accessed 1 Feb 2009

  11. OCR: Optical Character Recognition http://www.cdac.in/html/gist/research-areas/ocr.asp. Accessed 1 Feb 2009

  12. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5) (1988) 513–523

    Article  Google Scholar 

  13. Knecht, L.: PubMed: Truncation, Automatic Explosion, Mapping, and MeSH Headings, NLM Technical Bulletin 1998 May–June, 302 (1998)

    Google Scholar 

  14. Sowa, J.F. Semantic networks. In: Shapiro, S.C. (ed.) Encyclopedia of Artificial Intelligence, Wiley. http://www.jfsowa.com/pubs/semnet.htm (1992). Accessed 1 Feb 2009

  15. Biswas, A., Mohan, S., Mahapatra, R.: Search co-ordination with semantic routed network. In: Proceedings of the 18th International Conference on Computer Communications and Networks, US Virgin Islands (2009)

    Google Scholar 

  16. Watts, D.J.: Six Degrees: The Science of A Connected Age. W.W. Norton & Company, New York (2003)

    Google Scholar 

  17. Biswas, A., Mohan, S., Mahapatra, R.: Optimization of semantic routing table. In: Proceedings of the 17th International Conference on Computer Communications and Networks, US Virgin Islands (2008)

    Google Scholar 

  18. Culicover, P.W., Jackendoff, R.: Simpler Syntax. Oxford linguistics, Oxford University Press, Oxford (2005)

    Book  Google Scholar 

  19. Bai, C., Bornkessel-Schlesewsky, I., Wang, L., Hung, Y., Schlesewsky, M., Burkhardt, P.: Semantic composition engenders an N400: Evidence from Chinese compounds. NeuroReport 19(6) (2008) 695

    Article  Google Scholar 

  20. Brennan, J., Pylkknen, L.: Semantic composition and inchoative coercion: An MEG study. In: Proceedings of 21st Annual CUNY Conference on Human Sentence Processing, University of North Carolina, Chapel Hill (2008)

    Google Scholar 

  21. Grodzinsky, Y. The neurology of syntax: Language use without Broca’s area. Behavioral and Brain Sciences 23(1) (2001) 1–21

    Article  Google Scholar 

  22. Piango, M.M.J.M.: The neural basis of semantic compositionality. In session hosted by the Yale Interdepartmental Neuroscience Program, Yale University (2006)

    Google Scholar 

  23. Murphy, G.: Comprehending complex concepts. Cognitive Science 12(4) (1988) 529–562

    Article  Google Scholar 

  24. Culicover, P.W., Jackendoff, R.: The simpler syntax hypothesis. Trends in Cognitive Sciences 10(9) (2006) 413–418

    Article  Google Scholar 

  25. Kirshner, H.S.: Language studies in the third millennium. Brain and Language 71(1) (January 2000) 124–128

    Article  Google Scholar 

  26. Hagoort, P.: On Broca, brain, and binding: A new framework. Trends in Cognitive Sciences 9(9) (2005) 416–423

    Article  Google Scholar 

  27. Caramazza, A., Berndt, R.S.: Semantic and syntactic processes in Aphasia: A review of the literature. Psychological Bulletin 85(4) (1978) 898–918

    Article  Google Scholar 

  28. Kuperberg, G.: Neural mechanisms of language comprehension: Challenges to syntax. Brain Research 1146 (2007) 23–49

    Article  Google Scholar 

  29. Friederici, A.D., Opitz, B., Cramon, D.Y.: Segregating semantic and syntactic aspects of processing in the human brain: An fMRI investigation of different word types cereb. Cortex 10 (2000) 698–705

    Article  Google Scholar 

  30. Ye, Z., Zhou, X.: Involvement of cognitive control in sentence comprehension: Evidence from erps. Brain Research 1203 (2008) 103–115

    Article  Google Scholar 

  31. Ekiert, M.: The bilingual brain. Working Papers in TESOL and Applied Linguistics 3(2) (2003)

    Google Scholar 

  32. Kim, K.H.S., Relkin, N.R., Hirsch, J.: Distinct cortical areas associated with native and second languages. Nature 338 (1997) 171–174

    Google Scholar 

  33. Pinango, M.: Understanding the architecture of language: The possible role of neurology. Trends in Cognitive Sciences 10(2) (2006) 49–51

    Article  Google Scholar 

  34. Zurif, E.: Syntactic and semantic composition. Brain and Language 71(1) (2000) 261–263

    Article  Google Scholar 

  35. Bickerton, D.: Language & Species, The University of Chicago Press, Chicago & London (1990)

    Google Scholar 

  36. Jackendoff, R.: Compounding in the parallel architecture and conceptual semantics. In: Lieber, R., Stekauer, P. (eds.) The Oxford Handbook of Compounding. Oxford Handbooks in Linguistics. Oxford University Press, Oxford (2009)

    Google Scholar 

  37. Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychological Review 82(6) (November 1975) 407–428

    Article  Google Scholar 

  38. Anderson, J.R.: A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior 22 (1983) 261–295

    Article  Google Scholar 

  39. Anderson, J.R., Pirolli, P.L. Spread of activation. Journal of Experimental Psychology: Learning, Memory, & Cognition 10 (1984) 791–799

    Article  Google Scholar 

  40. Saffran, E.: The Organization of semantic memory: In support of a distributed model. Brain and Language 71(1) (2000) 204–212

    Article  Google Scholar 

  41. Rodriguez, R.A.: Aspects of cognitive linguistics and neurolinguistics: Conceptual structure and category-specific semantic deficits. Estudios Ingleses de la Universidad Complutense, 12 (2004) 43–62

    Google Scholar 

  42. Rajapske, R., Denham, M.: Fast access to concepts in concept lattices via bidirectioanl associative memory. Neural Computation 17 (2005) 2291–2300

    Article  Google Scholar 

  43. Rajapske, R., Denham, M.: Text retrieval with more realistic concept matching and reinforcement learning. In Information Processing and Management 42 (2006) 1260–1275

    Article  Google Scholar 

  44. Andersen, C.: A Computational model of complex concept composition. Master’s thesis, Department of Computer Science, University of Texas at Austin (1996)

    Google Scholar 

  45. Biswas, A., Mohan, S., Panigrahy, J., Tripathy, A., Mahapatra, R.: Representation and comparison of complex concepts for semantic routed network, In: Proceedings of 10th International Conference on Distributed Computing and Networking (ICDCN). Hyderabad (2009)

    Google Scholar 

  46. Wolff, K.E.: A first course in formal concept analysis, F. Faulbaum StatSoft ’93, 429–438, Gustav Fischer Verlag (2004)

    Google Scholar 

  47. Qi, J., Wei, L., Bai, Y.: Composition of concept lattices. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, Kunming (July 2008)

    Google Scholar 

  48. Murphy, G.L., Medin, D.L.: The role of theories in conceptual coherence, in Psychological Review (1985)

    Google Scholar 

  49. Foggia, P., Sansone, C., Vento, M.: A performance comparison of five algorithms for graph isomorphism, 3rd IAPR TC-15 workshop on graph-based representations in Pattern Recognition (2001) 188–199

    Google Scholar 

  50. Ogata, H., Fujibuchi, W., Goto, S., Kanehisa, M.: A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Research 28(20) (2000) 4021–4028

    Article  Google Scholar 

  51. Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Proceedings of ACL-08: HLT, Association for Computational Linguistics, Columbus, Ohio (2008) 236–244

    Google Scholar 

  52. Widdows, D.: Semantic vector products: Some initial investigations. In: Quantum Interaction: Papers from the Second International Symposium, Oxford (2008)

    Google Scholar 

  53. Widdows, D. Geometric ordering of concepts, logical disjunction, and learning by induction. Compositional Connectionism in Cognitive Science, AAAI Fall Symposium Series, Washington, DC, October (2004) 22–24

    Google Scholar 

  54. Widdows, D. Orthogonal negation in vector spaces for modeling word meanings and document retrieval. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL), http://acl.ldc.upenn.edu/acl2003/main/ps/Widdows.ps (2003). Accessed 1 Feb 2009

  55. Lin, D.: An information-theoretic definition of similarity. In: Shavlik, J.W (ed.) Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA (1998) 296–304

    Google Scholar 

  56. Rodríguez, A.: Semantic Similarity Among Spatial Entity Classes Ph.D. thesis, Department of Spatial Information Science and Engineering University of Maine (2000)

    Google Scholar 

  57. Veksler, V., Govostes, R., Gray, W.: Defining the dimensions of the human semantic space. In: Proceedings of the 30th Annual Meeting of the Cognitive Science Society, Austin, TX (2008)

    Google Scholar 

  58. Lindsey, R., Stipicevic, M.V.V.: BLOSSOM: Best path length on a semantic self-organizing map. In: Proceedings of the 30th Annual Meeting of the Cognitive Science Society. Washington, DC (2008)

    Google Scholar 

  59. Cederberg, S., Widdows, D.: Using LSA and noun coordination information to improve the precision and recall of automatic hyponymy extraction. In: Proceedings of Conference on Natural Language Learning (CoNLL), Edmonton, Canada (2003) 111–118

    Google Scholar 

  60. Maguitman, A.G., Menczer, F., Roinestad, H., Vespignani, A.: Algorithmic detection of semantic similarity. In: Proceedings of the 14th International Conference on World Wide Web (WWW ‘05) ACM, New York, NY (2005) 107–116

    Google Scholar 

  61. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. Systems, Man and Cybernetics, IEEE Transactions 19(1) (1989) 17–30

    Google Scholar 

  62. Jeh, G.: Simrank: A measure of structural-context similarity. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada (2002)

    Google Scholar 

  63. Yang, D., Powers, D.M.: Measuring semantic similarity in the taxonomy of WordNet. In: Proceedings of the 28th Australasian Conference on Computer Science, Newcastle, Australia 38 (2005)

    Google Scholar 

  64. Cilibrasi, R.L., Vitanyi, P.M.B.: The Google similarity distance. In: IEEE Transactions on Knowledge and Data Engineering 19(3) (2007) 370–383

    Article  Google Scholar 

  65. Widdows, D.: Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology – Vol 1, North American Chapter of the Association For Computational Linguistics. Association for Computational Linguistics. Morristown, NJ (2003) 197–204

    Google Scholar 

  66. Lemaire, B., Denhière, G.: Incremental construction of an associative network from a corpus. In: Proceedings of the 26th Annual Meeting of the Cognitive Science Society, Hillsdale, NJ (2004) 825–830

    Google Scholar 

  67. Dorow, B., Widdows, D., Ling, K., Eckmann, J.P., Sergi, D., Moses, E.: Using curvature and Markov clustering in graphs for lexical acquisition and word sense discrimination. In: Proceedings of the 2nd Workshop organized by the MEANING Project (MEANING 2005), Las Vegas, Nevada, USA February 3–4 (2005)

    Google Scholar 

  68. Borgida, A., Walsh, T., Hirsh, H.: Towards measuring similarity in description logics. In: Proceedings of the 2005 International Workshop on Description Logics (2005)

    Google Scholar 

  69. Hau, J., Lee, W., Darlington, J.: A semantic similarity measure for semantic web services. In: Web Service Semantics: Towards Dynamic Business Integration, Workshop at WWW, London, UK volume 5 (2005)

    Google Scholar 

  70. d’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. In: Proceedings of Convegno Italiano di Logica Computazionale (CILC05), Rome, Italy (2005)

    Google Scholar 

  71. Janowicz, K. Sim-DL: Towards a Semantic Similarity Measurement Theory for the Description Logic ALCNR in Geographic Information Retrieval On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops, Montpellier, France (2006) 1681–1692

    Google Scholar 

  72. Resource Description Framework (RDF): http://www.w3.org/RDF/. Accessed 1 Feb 2009

  73. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipediabased explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India (January 2007)

    Google Scholar 

  74. Widdows, D.: A mathematical model for context and word-meaning. Lecture Notes in Computer Science (2003) 369–382

    Google Scholar 

  75. Bottini, N. et al.: A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nature Genetics 36 (2004) 337–338

    Article  Google Scholar 

  76. Gene Ontology, http://www.geneontology.org/. Accessed 1 Feb 2009

  77. Disease Ontology, http://diseaseontology.sourceforge.net/. Accessed 1 Feb 2009

  78. Irgens, F.: Tensors. In: Continuum Mechanics. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  79. Broder, A., Mitzenmacher, M.: Network applications of Bloom filters: A survey. In Internet Mathematics 1(4) (2002) 485–509

    Article  Google Scholar 

  80. Ripeanu, M., Iamnitchi, A.: Bloom Filters – Short Tutorial, Computer Science Department, University of Chicago. http://www.cs.uchicago.edu/∼matei/PAPERS/bf.doc (2001). Accessed 1 Feb 2009

  81. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web, Scientific American Magazine. http://www.sciam.com/article.cfm?id=the-semantic-web&print=true. Retrieved on 26 March 2008 (2001). Accessed 1 Feb 2009

  82. Tempich, C., Staab, S., Wranik, A.: Remindin’: Semantic query routing in peer-to-peer networks based on social metaphors. In: Proceedings of the 13th International Conference on World Wide Web. WWW ’04. (2004) 640–649

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Texas A&M University, College Station, Texas, TX, USA

    Amitava Biswas, Suneil Mohan & Rabi Mahapatra

Authors
  1. Amitava Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suneil Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rabi Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amitava Biswas .

Editor information

Editors and Affiliations

  1. College of Computer Science, Zhejiang University, Zheda Road 38, Hangzhou, 310058, China, People's Republic

    Huajun Chen

  2. Drug Discovery Pte Ltd., Lilly Singapore Centre for, Biomedical Grove 8A, Singapore, 138648, Singapore

    Yimin Wang

  3. Center for Medical Informatics, Yale University School of Medicine, Cedar St. 333, New Haven, 06520-8009, Connecticut, USA

    Kei-Hoi Cheung

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Biswas, A., Mohan, S., Mahapatra, R. (2010). Semantic Technologies for Searching in e-Science Grids. In: Chen, H., Wang, Y., Cheung, KH. (eds) Semantic e-Science. Annals of Information Systems, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5908-9_5

Download citation

Publish with us

Policies and ethics

Search

Navigation