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Contextualization and Personalization of Queries to Knowledge Bases Using Spreading Activation

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8132)

Abstract

Most taxonomies and thesauri offer their users a huge amount of structured data. However, this volume of data is often excessive, and, thus does not fulfill the needs of the users, who are trying to find specific information related to a certain concept. While there are techniques that may partially alleviate this problem (e.g. visual representation of the data), some of the effects of the information overload persist. This paper proposes a four-step mechanism for personalization and knowledge extraction, derived from the information about users’ activities stored in their profiles. More precisely, the system extracts contextualization from the users’ profiles by using a spreading activation algorithm. The preliminary results of this approach are presented in this paper.

Keywords

  • Knowledge Base
  • Recommender System
  • Spreading Activation
  • Karstic Region
  • Placer Mine

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. Navigli, R., Ponzetto, S.P.: Babelnet: Building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 216–225. Association for Computational Linguistics (2010)

    Google Scholar 

  2. Auer, S., et al.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  3. Fellbaum, C.: Wordnet. In: Theory and Applications of Ontology: Computer Applications, pp. 231–243 (2010)

    Google Scholar 

  4. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to wordnet: An on-line lexical database*. International Journal of Lexicography 3(4), 235–244 (1990)

    CrossRef  Google Scholar 

  5. Reimerink, A., Faber, P.: Ecolexicon: A frame-based knowledge base for the environment. In: Proceedings of the International Conference Towards eEnvironment, pp. 25–27 (2009)

    Google Scholar 

  6. Ferragina, P., Gulli, A.: A personalized search engine based on web-snippet hierarchical clustering. Software: Practice and Experience 38(2), 189–225 (2008)

    CrossRef  Google Scholar 

  7. Han, L., Chen, G.: A fuzzy clustering method of construction of ontology-based user profiles. Advances in Engineering Software 40(7), 535–540 (2009)

    MATH  CrossRef  Google Scholar 

  8. Leung, K.T., Ng, W., Lee, D.L.: Personalized concept-based clustering of search engine queries. IEEE Transactions on Knowledge and Data Engineering 20(11), 1505–1518 (2008)

    CrossRef  Google Scholar 

  9. Widyantoro, D., Yen, J.: Using fuzzy ontology for query refinement in a personalized abstract search engine. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 1, pp. 610–615 (July 2001)

    Google Scholar 

  10. Kim, K.J., Cho, S.B.: Personalized mining of web documents using link structures and fuzzy concept networks. Applied Soft Computing 7(1), 398–410 (2007)

    CrossRef  Google Scholar 

  11. Duong, T.H., Uddin, M.N., Li, D., Jo, G.S.: A Collaborative Ontology-Based User Profiles System. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 540–552. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  12. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Communications of the ACM 43(8), 142–151 (2000)

    CrossRef  Google Scholar 

  13. Mulvenna, M.D., Anand, S.S., Büchner, A.G.: Personalization on the net using web mining: introduction. Communications of the ACM 43(8), 122–125 (2000)

    CrossRef  Google Scholar 

  14. Jiang, X., Tan, A.H.: Learning and inferencing in user ontology for personalized Semantic Web search. Information Sciences 179(16), 2794–2808 (2009)

    MATH  CrossRef  Google Scholar 

  15. Katifori, A., Vassilakis, C., Dix, A.: Ontologies and the brain: Using spreading activation through ontologies to support personal interaction. Cognitive Systems Research 11(1), 25–41 (2010)

    CrossRef  Google Scholar 

  16. Anderson, J.R.: A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior 22(3), 261–295 (1983)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  18. Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)

    CrossRef  Google Scholar 

  19. Crestani, F.: Application of spreading activation techniques in information retrieval. Artificial Intelligence Review (1997)

    Google Scholar 

  20. Alvarez, J.M., Polo, L., Jimenez, W., Abella, P., Labra, J.E.: Application of the spreading activation technique for recommending concepts of well-known ontologies in medical systems. In: Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011, p. 626 (2011)

    Google Scholar 

  21. Preece, S.E.: Spreading activation network model for information retrieval. Dissertation Abstracts International Part B: Science and Engineering (Diss. Abst. Int. Pt. B- Sci. & Eng.) 42(9) (1982)

    Google Scholar 

  22. Gouws, S., Rooyen, G.J.V., Engelbrecht, H.A.: Measuring conceptual similarity by spreading activation over wikipedia’s hyperlink structure, 46–54 (August 2010)

    Google Scholar 

  23. Crestani, F., Lee, P.L.: Searching the web by constrained spreading activation. Information Processing & Management 36(4), 585–605 (2000)

    CrossRef  Google Scholar 

  24. Sieg, A., Mobasher, B., Burke, R.: Ontological User Profiles for Representing Context in Web Search. In: 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, pp. 91–94. IEEE (November 2007)

    Google Scholar 

  25. Sieg, A., Mobasher, B., Burke, R.: Improving the effectiveness of collaborative recommendation with ontology-based user profiles. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, pp. 39–46. ACM Press, New York (2010)

    CrossRef  Google Scholar 

  26. Sieg, A., Mobasher, B., Burke, R.: Ontology-Based Collaborative Recommendation. In: ITWP (2010)

    Google Scholar 

  27. Teufl, P., Payer, U., Parycek, P.: Automated analysis of e-participation data by utilizing associative networks, spreading activation and unsupervised learning. In: Macintosh, A., Tambouris, E. (eds.) ePart 2009. LNCS, vol. 5694, pp. 139–150. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  28. Collins, C., Carpendale, S.: Vislink: Revealing relationships amongst visualizations. IEEE Transactions on Visualization and Computer Graphics 13(6), 1192–1199 (2007)

    CrossRef  Google Scholar 

  29. Kuß, A., Prohaska, S., Meyer, B., Rybak, J., Hege, H.C.: Ontology-based visualization of hierarchical neuroanatomical structures. In: Proc. Vis. Comp. Biomed., pp. 177–184 (2008)

    Google Scholar 

  30. Eyharabide, V., Amandi, A.: Ontology-based user profile learning. Applied Intelligence 36(4), 857–869 (2011)

    CrossRef  Google Scholar 

  31. Martín-Bautista, M.J., Kraft, D.H., Vila, M., Chen, J., Cruz, J.: User profiles and fuzzy logic for web retrieval issues. Soft Computing-A Fusion of Foundations, Methodologies and Applications 6(5), 365–372 (2002)

    MATH  Google Scholar 

  32. León Araúz, P., Magaña Redondo, P.: Ecolexicon: contextualizing an environmental ontology. In: Proceedings of the Terminology and Knowledge Engineering (TKE) Conference 2010, pp. 341–355 (2010)

    Google Scholar 

  33. Hagberg, A.A., Schult, D.A., Swart, P.J.: Exploring network structure, dynamics, and function using networkx. In: Varoquaux, G., Vaught, T., Millman, J. (eds.) Proceedings of the 7th Python in Science Conference, Pasadena, CA, USA, pp. 11–15 (2008)

    Google Scholar 

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Pelegrina, A.B., Martin-Bautista, M.J., Faber, P. (2013). Contextualization and Personalization of Queries to Knowledge Bases Using Spreading Activation. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_58

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  • DOI: https://doi.org/10.1007/978-3-642-40769-7_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40768-0

  • Online ISBN: 978-3-642-40769-7

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