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Bisociative Knowledge Discovery pp 66–90Cite as

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Bridging Concept Identification for Constructing Information Networks from Text Documents

Bridging Concept Identification for Constructing Information Networks from Text Documents

  • Matjaž Juršič5,
  • Borut Sluban5,
  • Bojan Cestnik5,6,
  • Miha Grčar5 &
  • …
  • Nada Lavrač5,7 
  • Chapter
  • Open Access
  • 8843 Accesses

  • 9 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7250)

Abstract

A major challenge for next generation data mining systems is creative knowledge discovery from diverse and distributed data sources. In this task an important challenge is information fusion of diverse mainly unstructured representations into a unique knowledge format. This chapter focuses on merging information available in text documents into an information network – a graph representation of knowledge. The problem addressed is how to efficiently and effectively produce an information network from large text corpora from at least two diverse, seemingly unrelated, domains. The goal is to produce a network that has the highest potential for providing yet unexplored cross domain links which could lead to new scientific discoveries. The focus of this work is better identification of important domain bridging concepts that are promoted as core nodes around which the rest of the network is formed. The evaluation is performed by repeating a discovery made on medical articles in the migraine magnesium domain.

Keywords

  • Knowledge Discovery
  • Text Mining
  • Bridging Concept Identification
  • Information Networks
  • PubMed
  • Migraine
  • Magnesium

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References

  1. Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)

    CrossRef  MathSciNet  Google Scholar 

  2. Bales, M.E., Johnson, S.B.: Graph theoretic modeling of large scale semantic networks. Journal of Biomedical Informatics 39(4), 451–464 (2006)

    CrossRef  Google Scholar 

  3. Berthold, M.R., Dill, F., Kötter, T., Thiel, K.: Supporting Creativity: Towards Associative Discovery of New Insights. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 14–25. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  4. Segond, M., Borgelt, C.: “BisoNet” Generation using Textual Data. In: Proceedings of Workshop on Explorative Analytics of Information Networks at ECML PKDD (2009)

    Google Scholar 

  5. Boström, H., Andler, S.F., Brohede, M., Johansson, R., Karlsson, A., van Laere, J., Niklasson, L., Nilsson, M., Persson, A., Ziemke, T.: On the definition of information fusion as a field of research. Technical report, University of Skovde, School of Hum.and Inf., Skovde, Sweden (2007)

    Google Scholar 

  6. Dubitzky, W., Kötter, T., Schmidt, O., Berthold, M.R.: Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 11–32. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  7. Dura, E., Gawronska, B., Olsson, B., Erlendsson, B.: Towards Information Fusion in Pathway Evaluation: Encoding Relations in Biomedical Texts. In: Proceedings of the 9th International Conference on Information Fusion (2006)

    Google Scholar 

  8. Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press (2007)

    Google Scholar 

  9. Fortuna, B., Lavrač, N., Velardi, P.: Advancing Topic Ontology Learning through Term Extraction. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 626–635. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  10. Juršič, M., Mozetič, I., Lavrač., N.: Learning Ripple Down Rules for Efficient Lemmatization. In: Proceedings of the 10th International Multiconference Information Society 2007, vol. A, pp. 206–209 (2007)

    Google Scholar 

  11. Koestler, A.: The Act of Creation. The Macmillan Co. (1964)

    Google Scholar 

  12. Kötter, T., Berthold, M.R.: From Information Networks to Bisociative Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 33–50. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  13. Ohsawa, Y., Benson, N.E., Yachida, M.: KeyGraph: Automatic Indexing by Co occurrence Graph based on Building Construction Metaphor. In: Proceedings of the Advances in Digital Libraries Conference (ADL), pp. 12–18 (1998)

    Google Scholar 

  14. Petric, I., Urbancic, T., Cestnik, B., Macedoni Luksic, M.: Literature mining method RaJoLink for uncovering relations between biomedical concepts. Journal of Biomedical Informatics 42(2), 219–227 (2009)

    CrossRef  Google Scholar 

  15. Petrič, I., Cestnik, B., Lavrač, N., Urbančič, T.: Outlier Detection in Cross Context Link Discovery for Creative Literature Mining. Comput. J., November 2 (2010)

    Google Scholar 

  16. Petrič, I., Cestnik, B., Lavrač, N., Urbančič, T.: Bisociative Knowledge Discovery by Literature Outlier Detection. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 313–324. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  17. Porter, M.F.: An algorithm for suffix stripping. Progr. 14(3), 130–137 (1980)

    CrossRef  Google Scholar 

  18. Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42(3), 203–231 (2001)

    CrossRef  Google Scholar 

  19. Racunas, S., Griffin, C.: Logical data fusion for biological hypothesis evaluation. In: Proceedings of the 8th International Conference on Information Fusion (2005)

    Google Scholar 

  20. Sluban, B., Juršič, M., Cestnik, B., Lavrač, N.: Exploring the Power of Outliers for Cross-domain Literature Mining. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 325–337. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  21. Smalheiser, N.R., Swanson, D.R.: Using ARROWSMITH: a computer assisted approach to formulating and assessing scientific hypotheses. Comput Methods Programs Biomed. 57(3), 149–153 (1998)

    CrossRef  Google Scholar 

  22. Smirnov, A., Pashkin, M., Shilov, N., Levashova, T., Krizhanovsky, A.: Intelligent Support for Distributed Operational Decision Making. In: Proceedings of the 9th International Conference on Information Fusion (2006)

    Google Scholar 

  23. Srinivasan, P., Libbus, B., Sehgal, A.K.: Mining MEDLINE: Postulating a beneficial role for curcumin longa in retinal diseases. In: Hirschman, L., Pustejovsky, J. (eds.) BioLINK 2004: Linking Biological Literature, Ontologies, and Databases, Boston, Massachusetts, pp. 33–40 (2004)

    Google Scholar 

  24. Swanson, D.R.: Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine 31(4), 526–557 (1988)

    CrossRef  Google Scholar 

  25. Swanson, D.R.: Medical literature as a potential source of new knowledge. Bull. Med. Libr. Assoc. 78(1), 29–37 (1990)

    Google Scholar 

  26. Swanson, D.R., Smalheiser, N.R., Torvik, V.I.: Ranking Indirect Connections in Literature Based Discovery: The Role of Medical Subject Headings (MeSH). Journal of the American Society for Inf. Science and Technology 57, 1427–1439 (2006)

    CrossRef  Google Scholar 

  27. Urbančič, T., Petrič, I., Cestnik, B., Macedoni-Lukšič, M.: Literature Mining: Towards Better Understanding of Autism. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 217–226. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  28. Weeber, M., Vos, R., Klein, H., de Jong van den Berg, L.T.W.: Using concepts in literature based discovery: Simulating Swanson’s Raynaud–fish oil and migraine–magnesium discoveries. J. Am. Soc. Inf. Sci. Tech. 52(7), 548–557 (2001)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Matjaž Juršič, Borut Sluban, Bojan Cestnik, Miha Grčar & Nada Lavrač

  2. Temida d.o.o., Ljubljana, Slovenia

    Bojan Cestnik

  3. University of Nova Gorica, Nova Gorica, Slovenia

    Nada Lavrač

Authors
  1. Matjaž Juršič
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  2. Borut Sluban
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  3. Bojan Cestnik
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  4. Miha Grčar
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  5. Nada Lavrač
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Editor information

Editors and Affiliations

  1. Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Michael R. Berthold

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Juršič, M., Sluban, B., Cestnik, B., Grčar, M., Lavrač, N. (2012). Bridging Concept Identification for Constructing Information Networks from Text Documents. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_6

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