Abstract
In literature mining, the identification of bridging concepts that link two diverse domains has been shown to be a promising approach for finding bisociations as distinct, yet unexplored cross-domain connections which could lead to new scientific discoveries. This chapter introduces the system CrossBee (on line Cross-Context Bisociation Explorer) which implements a methodology that supports the search for hidden links connecting two different domains. The methodology is based on an ensemble of specially tailored text mining heuristics which assign the candidate bridging concepts a bisociation score. Using this score, the user of the system can primarily explore only the most promising concepts with high bisociation scores. Besides improved bridging concept identification and ranking, CrossBee also provides various content presentations which further speed up the process of bisociation hypotheses examination. These presentations include side-by-side document inspection, emphasizing of interesting text fragments, and uncovering similar documents. The methodology is evaluated on two problems: the standard migraine-magnesium problem well-known in literature mining, and a more recent autism-calcineurin literature mining problem.
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References
Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
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)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web. In: Proc. of the 10th int. Conference on World Wide Web, pp. 613–622 (2001)
Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press (2007)
Hoi, S.C.H., Jin, R.: Semi-Supervised Ensemble Ranking. In: Proc. of the 23rd National Conference on Artificial Intelligence, vol. 2. AAAI Press (2008)
Juršič, M., Sluban, B., Cestnik, B., Grčar, M., Lavrač, N.: Bridging Concept Identification for Constructing Information Networks from Text Documents. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 66–90. Springer, Heidelberg (2012)
Koestler, A.: The Act of Creation. The Macmillan Co. (1964)
Li, D., Wang, Y., Ni, W., Huang, Y., Xie, M.: An Ensemble Approach to Learning to Rank. In: 5th Int. Conf. on Fuzzy Systems and Knowledge Discovery, pp. 101–105 (2008)
Macedoni Lukšič, M., Petrič, I., Cestnik, B., Urbančič, T.: Developing a Deeper Understanding of Autism: Connecting Knowledge through Literature Mining. In: Autism Research and Treatment (2011)
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)
Petrič, I., Cestnik, B., Lavrač, N., Urbančič, T.: Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining. Comput. J., November 2 (2010)
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)
Rokach, L.: Ensemble-based classifiers. Art. Int. Review 33(1-2), 1–39 (2010)
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)
Swanson, D.R.: Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine 31(4), 526–557 (1988)
Swanson, D.R.: Medical literature as a potential source of new knowledge. Bull. Med. Libr. Assoc. 78(1), 29–37 (1990)
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)
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)
Urbančič, T., Petrič, I., Cestnik, B., Macedoni Lukšič, M.: RaJoLink: A Method for Finding Seeds of Future Discoveries in Nowadays. In: Proceedings of the 18th Symposium on Methodologies for Intelligent Systems, Prague, pp. 129–138 (2009)
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)
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Juršič, M., Cestnik, B., Urbančič, T., Lavrač, N. (2012). Bisociative Literature Mining by Ensemble Heuristics. 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_24
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