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Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of Its Field

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Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)

Abstract

We describe a novel method for the analysis of research activities of an organization by mapping that to a taxonomy tree of the field. The method constructs fuzzy membership profiles of the organizationmembers or teams in terms of the taxonomy’s leaves (research topics), and then it generalizes them in two steps. These steps are: (i) fuzzy clustering research topics according to their thematic similarities in the department, ignoring the topology of the taxonomy, and (ii) optimally lifting clusters mapped to the taxonomy tree to higher ranked categories by ignoring “small” discrepancies. We illustrate the method by applying it to data collected by using an in-house e-survey tool from a university department and from a university research center. The method can be considered for knowledge generalization over any taxonomy tree.

Keywords

Fuzzy Cluster Fuzzy Membership Spectral Cluster Subject Cluster Fuzzy Cluster Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    ACM Computing Classification System (1998), http://www.acm.org/about/class/1998 (cited September 9, 2008)
  2. 2.
    Advanced Visual Systems (AVS), http://www.avs.com/solutions/avs-powerviz/utility_distribution.html (cited November 27, 2010)
  3. 3.
    Beneventano, D., Dahlem, N., El Haoum, S., Hahn, A., Montanari, D., Reinelt, M.: Ontology-driven semantic mapping. In: Enterprise Interoperability III, Part IV, pp. 329–341. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    Bezdek, J., Hathaway, R.J., Windham, M.P.: Numerical comparisons of the RFCM and AP algorithms for clustering relational data. Pattern Recognition 24, 783–791 (1991)Google Scholar
  5. 5.
    Bezdek, J., Keller, J., Krishnapuram, R., Pal, T.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers (1999)Google Scholar
  6. 6.
    Brouwer, R.: A method of relational fuzzy clustering based on producing feature vectors using FastMap. Information Sciences 179, 3561–3582 (2009)CrossRefGoogle Scholar
  7. 7.
    Buche, P., Dibie-Barthelemy, J., Ibanescu, L.: Ontology mapping using fuzzy conceptual graphs and rules. In: ICCS Supplement, vol. 1724 (2008)Google Scholar
  8. 8.
    Cali, A., Gottlob, G., Pieris, A.: Advanced processing for ontological queries. Proceedings of the VLDB Endowment 3(1), 554–565 (2010)Google Scholar
  9. 9.
    Davé, R.N., Sen, S.: Robust fuzzy clustering of relational data. IEEE Transactions on Fuzzy Systems 10, 713–727 (2002)CrossRefGoogle Scholar
  10. 10.
    Ding, Y., Foo, S.: Ontology research and development. Journal of Information Science 28(5), 375–388 (2002)Google Scholar
  11. 11.
    Dotan-Cohen, D., Kasif, S., Melkman, A.: Seeing the forest for the trees: using the gene ontology to restructure hierarchical clustering. Bioinformatics 25(14), 1789–1795 (2009)CrossRefGoogle Scholar
  12. 12.
    Eick, S.G.: Visualizing online activity. Communications of the ACM 44(8), 45–50 (2001)CrossRefGoogle Scholar
  13. 13.
    Feather, M., Menzies, T., Connelly, J.: Matching software practitioner needs to researcher activities. In: Proc. of the 10th Asia-Pacific Software Engineering Conference (APSEC 2003), vol. 6, IEEE (2003)Google Scholar
  14. 14.
    Freudenberg, J.M., Joshi, V.K., Hu, Z., Medvedovic, M.: CLEAN: CLustering Enrichment ANalysis. BMC Bioinformatics 10, 234 (2009)CrossRefGoogle Scholar
  15. 15.
    Gahegan, M., Agrawal, R., Jaiswal, A., Luo, J., Soon, K.-H.: A platform for visualizing and experimenting with measures of semantic similarity in ontologies and concept maps. Transactions in GIS 12(6), 713–732 (2008)CrossRefGoogle Scholar
  16. 16.
    Gaevic, D., Hatala, M.: Ontology mappings to improve learning resource search. British Journal of Educational Technology 37(3), 375–389 (2006)CrossRefGoogle Scholar
  17. 17.
    Georgeon, O.L., Mille, A., Bellet, T., Mathern, B., Ritter, F.: Supporting activity modeling from activity traces. Expert Systems: The Journal of Knowledge Engineering (2010) (submitted)Google Scholar
  18. 18.
    The Gene Ontology Consortium. The Gene Ontology project in 2008. Nucleic Acids Research 36 (database issue), D4404 (2008); doi:10.1093/nar/gkm883, PMID 17984083Google Scholar
  19. 19.
    Ghazvinian, A., Noy, N., Musen, M.: Creating mappings for ontologies in Biomedicine: simple methods work. In: AMIA 2009 Symposium Proceedings, pp. 198–202 (2009)Google Scholar
  20. 20.
    Guh, Y.Y., Yang, M.S., Po, R.W., Lee, E.S.: Establishing performance evaluation structures by fuzzy relation-based cluster analysis. Computers and Mathematics with Applications 56, 572–582 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Hathaway, R.J., Davenport, J.W., Bezdek, J.C.: Relational duals of the c-means algorithms. Pattern Recognition 22, 205–212 (1989)zbMATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    Hathaway, R.J., Bezdek, J.C.: NERF c-means: Non-Euclidean relational fuzzy clustering. Pattern Recognition 27, 429–437 (1994)CrossRefGoogle Scholar
  23. 23.
    Huang, L., Yan, D., Jordan, M.I., Taft, N.: Spectral clustering with perturbed data. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, vol. 21, pp. 705–712. MIT Press (2009)Google Scholar
  24. 24.
    Hubert, L.J., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985)CrossRefGoogle Scholar
  25. 25.
    Liu, J., Wang, W., Yang, J.: Gene ontology friendly biclustering of expression profiles. In: Proc. of the IEEE Computational Systems Bioinformatics Conference, pp. 436–447. IEEE (2004)Google Scholar
  26. 26.
    von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17, 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Marinica, C., Guillet, F.: Improving post-mining of association rules with ontologies. In: The XIII International Conference Applied Stochastic Models and Data Analysis (ASMDA), pp. 76–80 (2009); ISBN 978-9955-28-463-5Google Scholar
  28. 28.
    Mazza, R.: Introduction to Information Visualization, pp. 978–971. Springer, Heidelberg (2009); ISBN: 978-1-84800-218-0Google Scholar
  29. 29.
    McLachlan, G.J., Khan, N.: On a resampling approach for tests on the number of clusters with mixture model based clustering of tissue samples. J. Multivariate Anal. 90, 90–105 (2004)zbMATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Miralaei, S., Ghorbani, A.: Category-based similarity algorithm for semantic similarity in multi-agent information sharing systems. In: IEEE/WIC/ACM Int. Conf. on Intelligent Agent Technology, pp. 242–245 (2005)Google Scholar
  31. 31.
    Mirkin, B.: Additive clustering and qualitative factor analysis methods for similarity matrices. Journal of Classification 4(1), 7–31 (1987)zbMATHMathSciNetCrossRefGoogle Scholar
  32. 32.
    Mirkin, B., Fenner, T., Galperin, M., Koonin, E.: Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes. BMC Evolutionary Biology 3(2) (2003)Google Scholar
  33. 33.
    Mirkin, B., Nascimento, S., Pereira, L.M.: Cluster-lift method for mapping research activities over a concept tree. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II. SCI, vol. 263, pp. 245–258. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  34. 34.
    Mirkin, B., Nascimento, S., Fenner, T., Pereira, L.M.: Constructing and Mapping Fuzzy Thematic Clusters to Higher Ranks in a Taxonomy. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS (LNAI), vol. 6291, pp. 329–340. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  35. 35.
    Mirkin, B., Nascimento, S.: Additive spectral method for fuzzy cluster analysis of similarity data including community structure and affinity matrices. Information Sciences 183, 16–34 (2012)CrossRefGoogle Scholar
  36. 36.
    Newman, M.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)CrossRefGoogle Scholar
  37. 37.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  38. 38.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Ditterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2002)Google Scholar
  39. 39.
    OWL 2 Web Ontology Language Overview (2009), http://www.w3.org/TR/2009/RECowl2overview20091027/ (cited November 27, 2010)
  40. 40.
    Roubens, M.: Pattern classification problems and fuzzy sets. Fuzzy Sets and Systems 1, 239–253 (1978)zbMATHMathSciNetCrossRefGoogle Scholar
  41. 41.
    Sato, M., Sato, Y., Jain, L.C.: Fuzzy Clustering Models and Applications. Physica-Verlag, Heidelberg (1997)zbMATHGoogle Scholar
  42. 42.
    Schattkowsky, T., Frster, A.: On the pitfalls of UML-2 activity modeling. In: International Workshop on Modeling in Software Engineering (MISE 2007), pp. 1–6 (2007)Google Scholar
  43. 43.
    Skarman, A., Jiang, L., Hornshoj, H., Buitenhuis, B., Hedegaard, J., Conley, L., Sorensen, P.: Gene set analysis methods applied to chicken microarray expression data. BMC Proceedings  3 (suppl. 4) (2009)Google Scholar
  44. 44.
    Shepard, R.N., Arabie, P.: Additive clustering: representation of similarities as combinations of overlapping properties. Psychological Review 86, 87–123 (1979)CrossRefGoogle Scholar
  45. 45.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  46. 46.
    SNOMED Clinical Terms (2010), http://www.nlm.nih.gov/research/umls/Snomed/snomed_main.html (cited November 27, 2010)
  47. 47.
    Sosnovsky, S., Mitrovic, A., Lee, D., Prusilovsky, P., Yudelson, M., Brusilovsky, V., Sharma, D.: Towards integration of adaptive educational systems: mapping domain models to ontologies. In: Dicheva, D., Harrer, A., Mizoguchi, R. (eds.), Procs. of 6th International Workshop on Ontologies and Semantic Web for ELearning (SWEL 2008) at ITS 2008 (2008), http://compsci.wssu.edu/iis/swel/SWEL08/Papers/Sosnovsky.pdf
  48. 48.
    Thomas, H., O’Sullivan, D., Brennan, R.: Evaluation of ontology mapping representation. In: Proceedings of the Workshop on Matching and Meaning, pp. 64–68 (2009)Google Scholar
  49. 49.
    Windham, M.P.: Numerical classification of proximity data with assignment measures. Journal of Classification 2, 157–172 (1985)CrossRefGoogle Scholar
  50. 50.
    White, S., Smyth, P.: A spectral clustering approach to finding communities in graphs. In: SIAM International Conference on Data Mining (2005)Google Scholar
  51. 51.
    Thorne, C., Zhu, J., Uren, V.: Extracting domain ontologies with CORDER. Tech. Reportkmi-05-14. Open University, 1-15 (2005)Google Scholar
  52. 52.
    Yang, M.S., Shih, H.M.: Cluster analysis based on fuzzy relations. Fuzzy Sets and Systems 120, 197–212 (2001)zbMATHMathSciNetCrossRefGoogle Scholar
  53. 53.
    Yang, L., Ball, M., Bhavsar, V., Boley, H.: Weighted partonomy-taxonomy trees with local similarity measures for semantic buyer-seller match-making. Journal of Business and Technology 1(1), 42–52 (2005)Google Scholar
  54. 54.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)zbMATHMathSciNetCrossRefGoogle Scholar
  55. 55.
    Zhang, S., Wang, R.-S., Zhang, X.-S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374, 483–490 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceBirkbeck University of LondonLondonUK
  2. 2.School of Applied Mathematics and InformaticsHigher School of EconomicsMoscowRF
  3. 3.Department of Computer Science and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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