Discovering Relations between Indirectly Connected Biomedical Concepts

  • Dirk Weissenborn
  • Michael Schroeder
  • George Tsatsaronis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8574)


The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. In this work we attempt to address this problem by using indirect knowledge connecting two concepts in a graph to identify hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (text) data. In this graph we attempt to mine path patterns which potentially characterize a biomedical relation. For our experimental evaluation we focus on two frequent relations, namely “has target”, and “may treat”. Our results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8. Finally, analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations.


Relation Discovery Biomedical Concepts Text Mining 


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  1. 1.
    Swanson, D.: Fish oil, raynaud’s syndrome, and undiscovered public knowledge. Perspect. Bio. Med 30, 7–18 (1986)Google Scholar
  2. 2.
    Cohen, T., Schvaneveldt, R., Widdows, D.: Reflective random indexing and indirect inference: a scalable method for discovery of implicit connections. J. Biomed. Inform. 43(2), 240–256 (2010)CrossRefGoogle Scholar
  3. 3.
    Frijters, R., van Vugt, M., Smeets, R., van Schaik, R.C., de Vlieg, J., Alkema, W.: Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Computational Biology 6(9) (2010)Google Scholar
  4. 4.
    Goertzel, B., Goertzel, I.F., Pinto, H., Ross, M., Heljakka, A., Pennachin, C.: Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts. In: BioNLP, pp. 104–111 (2006)Google Scholar
  5. 5.
    Vizenor, L., Bodenreider, O., McCray, A.T.: Auditing associative relations across two knowledge sources. Journal of Biomedical Informatics 42(3), 426–439 (2009)CrossRefGoogle Scholar
  6. 6.
    Lao, N., Subramanya, A., Pereira, F., Cohen, W.W.: Reading the web with learned syntactic-semantic inference rules. In: EMNLP-CoNLL, pp. 1017–1026 (2012)Google Scholar
  7. 7.
    Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: AMIA Symposium, pp. 17–21 (2001)Google Scholar
  8. 8.
    Snow, R., Jurafsky, D., Ng, A.Y.: Learning syntactic patterns for automatic hypernym discovery. In: NIPS (2004)Google Scholar
  9. 9.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Google Scholar
  10. 10.
    Mimno, D.M., Hoffman, M.D., Blei, D.M.: Sparse stochastic inference for latent dirichlet allocation. In: ICML (2012)Google Scholar
  11. 11.
    McCallum, A., Schultz, K., Singh, S.: FACTORIE: Probabilistic programming via imperatively defined factor graphs. In: NIPS (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dirk Weissenborn
    • 1
  • Michael Schroeder
    • 1
  • George Tsatsaronis
    • 1
  1. 1.Biotechnology CenterTechnische Universität DresdenDresdenGermany

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