Abstract
The integration of heterogeneous data from various domains without the need for prefiltering prepares the ground for bisociative knowledge discoveries where attempts are made to find unexpected relations across seemingly unrelated domains. Information networks, due to their flexible data structure, lend themselves perfectly to the integration of these heterogeneous data sources. This chapter provides an overview of different types of information networks and categorizes them by identifying several key properties of information units and relations which reflect the expressiveness and thus ability of an information network to model heterogeneous data from diverse domains. The chapter progresses by describing a new type of information network known as bisociative information networks. This kind of network combines the key properties of existing networks in order to provide the foundation for bisociative knowledge discoveries. Finally based on this data structure three different patterns are described that fulfill the requirements of a bisociation by connecting concepts from seemingly unrelated domains.
Chapter PDF
Similar content being viewed by others
References
Abello, J., Korn, J.: Mgv: a system for visualizing massive multidigraphs. Transactions on Visualization and Computer Graphics 8(1), 21–38 (2002)
Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)
Auillans, P., de Mendez, P.O., Rosenstiehl, P., Vatant, B.: A Formal Model for Topic Maps. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 69–83. Springer, Heidelberg (2002)
Baitaluk, M., Qian, X., Godbole, S., Raval, A., Ray, A., Gupta, A.: Pathsys: integrating molecular interaction graphs for systems biology. BMC Bioinformatics 7, 55 (2006)
Bales, M.E., Johnson, S.B.: Graph theoretic modeling of large-scale semantic networks. Journal of Biomedical Informatics 39, 451–464 (2006)
Belew, R.: Adaptive information retrieval: using a connectionist representation to retrieve and learn about documents. In: Proceedings of the 12th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11–20 (1989)
Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., Morissette, J.: Bio2rdf: towards a mashup to build bioinformatics knowledge systems. Journal of Biomedical Informatics 41, 706–716 (2008)
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 5, 34–43 (2001)
Birkland, A., Yona, G.: Biozon: a system for unification, management and analysis of heterogeneous biological data. BMC Bioinformatics 7, 70 (2006)
Bodenreider, O.: Biomedical ontologies in action: role in knowledge management, data integration and decision support. IMIA Yearbook of Medical Informatics 1, 67–79 (2008)
Brandes, U., Erlebach, T.: Network Analysis: Methodological Foundations. Springer (2005)
Burgun, A., Bodenreider, O.: Accessing and integrating data and knowledge for biomedical research. IMIA Yearbook of Medical Informatics 1, 91–101 (2008)
Chen, H., Ding, L., Wu, Z., Yu, T., Dhanapalan, L., Chen, J.Y.: Semantic web for integrated network analysis in biomedicine. Briefings in Bioinformatics 10, 177–192 (2009)
Chen, H., Ng, T.: An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): Symbolic branch-and-bound search vs. connectionist hopfield net activation. Journal of the American Society for Information Science 46(5), 348–369 (1995)
Cheung, K.-H., Yip, K.Y., Smith, A., Deknikker, R., Masiar, A., Gerstein, M.: Yeasthub: a semantic web use case for integrating data in the life sciences domain. Bioinformatics 21(suppl.1), i85–i96 (2005)
Chua, H.N., Sung, W.-K., Wong, L.: An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics 23, 3364–3373 (2007)
Consortium, G.O.: Creating the gene ontology resource: design and implementation. Genome Research 11, 1425–1433 (2001)
Crestani, F.: Application of spreading activation techniques in information retrieval. Artificial Intelligence Review 11, 453–482, 12 (1997)
Dienhart, J.M.: A linguistic look at riddles. Journal of Pragmatics 31(1), 95–125 (1999)
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)
Durand, P., Labarre, L., Meil, A., Divol, J.-L., Vandenbrouck, Y., Viari, A., Wojcik, J.: Genolink: a graph-based querying and browsing system for investigating the function of genes and proteins. BMC Bioinformatics 7(1), 21 (2006)
Figeys, D.: Combining different ’omics’ technologies to map and validate protein-protein interactions in humans. Briefings in Functional Genomics and Proteomics 2, 357–365 (2004)
I.O. for Standardization. Information Technology – Document Description and Processing Languages – Topic Maps – Data Model. ISO, Geneva, Switzerland (2006)
Franke, L., van Bakel, H., Fokkens, L., de Jong, E.D., Egmont-Petersen, M., Wijmenga, C.: Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. The American Journal of Human Genetics 78, 1011–1025 (2006)
Furnas, G.W.: Generalized fisheye views. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, vol. 17(4), pp. 16–23 (1986)
Gavin, A.-C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dümpelfeld, B., Edelmann, A., Heurtier, M.-A., Hoffman, V., Hoefert, C., Klein, K., Hudak, M., Michon, A.-M., Schelder, M., Schirle, M., Remor, M., Rudi, T., Hooper, S., Bauer, A., Bouwmeester, T., Casari, G., Drewes, G., Neubauer, G., Rick, J.M., Kuster, B., Bork, P., Russell, R.B., Superti-Furga, G.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006)
Getoor, L., Diehl, C.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)
Han, J.: Mining Heterogeneous Information Networks by Exploring the Power of Links. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 13–30. Springer, Heidelberg (2009)
Hayes, J.: A graph model for RDF. Master’s thesis, Technische Universität Darmstadt, Dept. of Computer Science, Darmstadt, Germany. In: Collaboration with the Computer Science Dept., University of Chile, Santiago de Chile (2004)
Hayes, J., Gutierrez, C.: Bipartite Graphs as Intermediate Model for RDF. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 47–61. Springer, Heidelberg (2004)
Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003)
Kiemer, L., Costa, S., Ueffing, M., Cesareni, G.: Wi-phi: A weighted yeast interactome enriched for direct physical interactions. Proteomics 7, 932–943 (2007)
Koehler, J., Rawlings, C., Verrier, P., Mitchell, R., Skusa, A., Ruegg, A., Philippi, S.: Linking experimental results, biological networks and sequence analysis methods using ontologies and generalised data structures. Silico Biology 5, 33–44 (2005)
Koestler, A.: The Act of Creation. Macmillan (1964)
Kötter, T., Berthold, M.R.: (Missing) concept discovery in heterogeneous information networks. In: Proceedings of the 2nd International Conference on Computational Creativity, pp. 135–140 (2011)
Kötter, T., Berthold, M.R.: (Missing) Concept Discovery in Heterogeneous Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 230–245. Springer, Heidelberg (2012)
Kötter, T., Thiel, K., Berthold, M.R.: Domain bridging associations support creativity. In: Proceedings of the International Conference on Computational Creativity, pp. 200–204 (2010)
Kwoh, C.K., Ng, P.Y.: Network analysis approach for biology. Cellular and Molecular Life Sciences 64, 1739–1751 (2007)
Lakoff, G., Johnson, M.: Metaphors We Live by. University of Chicago Press (1980)
Lassila, O., Swick, R.R.: Resource Description Framework (RDF) model and syntax specification. W3C Working Draft (February 2002)
Li, J., Li, X., Su, H., Chen, H., Galbraith, D.W.: A framework of integrating gene relations from heterogeneous data sources: an experiment on arabidopsis thaliana. Bioinformatics 22(16), 2037–2043 (2006)
Martinez Morales, A.A.: A directed hypergraph model for RDF. In: Simperl, E., Diederich, J., Schreiber, G. (eds.) Proceedings of the KWEPSY 2007, vol. 275 (2007)
Nagel, U., Thiel, K., Kötter, T., Piątek, D., Berthold, M.R.: Bisociative Discovery of Interesting Relations between Domains. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 306–317. Springer, Heidelberg (2011)
Nagel, U., Thiel, K., Kötter, T., Piatek, D., Berthold, M.R.: Towards Discovery of Subgraph Bisociations. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 263–284. Springer, Heidelberg (2012)
Pavlopoulos, G., Wegener, A.-L., Schneider, R.: A survey of visualization tools for biological network analysis. BioData Mining 1(1), 1–12 (2008)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers (1988)
Pepper, S.: The tao of topic maps: finding the way in the age of infoglut. In: Proceedings of XML Europe (2000)
Schaeffer, S.E.: Graph clustering. Computer Science Review 1, 27–64 (2007)
Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link Discovery in Graphs Derived from Biological Databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35–49. Springer, Heidelberg (2006)
Shen, Z., Ma, K.-L., Eliassi-Rad, T.: Visual analysis of large heterogeneous social networks by semantic and structural abstraction. IEEE Transactions on Visualization and Computer Graphics 12(6), 1427–1439 (2006)
Smith, A.K., Cheung, K.-H., Yip, K.Y., Schultz, M., Gerstein, M.K.: Linkhub: a semantic web system that facilitates cross-database queries and information retrieval in proteomics. BMC Bioinformatics 8, S5 (2007)
Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J., Consortium, O.B.I., Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.-A., Scheuermann, R.H., Shah, N., Whetzel, P.L., Lewis, S.: The obo foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251–1255 (2007)
Thiel, K., Berthold, M.R.: Node similarities from spreading activation. In: Proceedings of the IEEE International Conference on Data Mining (2010)
Thiel, K., Berthold, M.R.: Node Similarities from Spreading Activation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 246–262. Springer, Heidelberg (2012)
Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B., Botstein, D.: A bayesian framework for combining heterogeneous data sources for gene function prediction (in saccharomyces cerevisiae). Proceedings of the National Academy of Sciences 100, 8348–8353 (2003)
Tzitzikas, Y., Constantopoulos, P., Spyratos, N.: Mediators over ontology-based information sources. In: Second International Conference on Web Information Systems Engineering, pp. 31–40 (2001)
van Ham, F., van Wijk, J.: Interactive visualization of small world graphs. In: van Wijk, J. (ed.) Proc. IEEE Symposium on Information Visualization INFOVIS 2004, pp. 199–206 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2012 The Author(s)
About this chapter
Cite this chapter
Kötter, T., Berthold, M.R. (2012). From Information Networks to Bisociative Information Networks. 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_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-31830-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31829-0
Online ISBN: 978-3-642-31830-6
eBook Packages: Computer ScienceComputer Science (R0)