Weisen Guo, Steven B. Kraines


Over one million papers are published annually in life sciences. Bioinformatics and knowledge discovery fields aim to help researchers conduct scientific discovery using the existing published knowledge. Existing literature-based discovery methods and tools mainly use text-mining techniques to extract non-specified relationships between two concepts. We present an approach that uses semantic web techniques to measure the relevance between two relationships with specified types that involve a particular entity. We consider two highly relevant relationships as a relationship association. Relationship associations could help researchers generate scientific hypotheses or create computer-interpretable semantic descriptors for their papers. The relationship association extraction process is described and the results of experiments for extracting relationship associations from 392 semantic graphs representing MEDLINE papers are presented


  1. Aoyama, S., Kamiya, R., 2005. Cyclical Interactions between Two Outer Doublet Microtubules in Split Flagellar Axonemes. Biophys J., 89 (5), 3261-3268.
  2. Cohen, W.W., Ravikumar, P., Fienberg, S.E., 2003. A Comparison of String Distance Metrics for NameMatching Tasks. Proceedings of the ACM Workshop on Data Cleaning, Record Linkage and Object Identification, Washington DC, August 2003.
  3. Gordon, M. D., Lindsay, R.K., 1996. Toward Discovery Support Systems: A Replication, Re-Examination, and Extension of Swanson's Work on Literature-Based Discovery of a Connection between Raynaud's and Fish Oil. JASIST, 47(2), 116-128.
  4. Guo, W., Kraines, S., 2008. Explicit Scientific Knowledge Comparison Based on Semantic Description Matching. American Society for Information Science and Technology 2008 Annual Meeting, Columbus, Ohio.
  5. Hristovski, D., Stare, J., Peterlin, B., Dzeroski, S., 2001. Supporing discovery in medicine by association rule mining in Medline and UMLS, Medinfo, 10(Pt2), 1344-1348.
  6. Hristovski, D., Peterlin, B., Mitchell, J.A., Humphrey, S.M., 2005. Using literature-based discovery to identify disease candidate genes. International Journal of Medical Informatics, 74(2-4), 289-298.
  7. King, T.J., Roberts, M.B.V, 1986. Biology: A Functional Approach. Thomas Nelson and Sons. ISBN 978- 0174480358.
  8. Kraines, S., Guo, W., Kemper, B., Nakamura, Y., 2006. EKOSS: A Knowledge-User Centered Approach to Knowledge Sharing, Discovery, and Integration on the Semantic Web. The 5th International Semantic Web Conference, LNCS 4273, 833-846.
  9. Kraines, S., 2009. An Ontology-based System for Sharing Expert Knowledge in Life Sciences. Journal of Web Semantics, in review.
  10. Langley, P., 2000. The computational support of scientific discovery. International Journal of Human-Computer Studies, 53, 393-410.
  11. Lindsay, R.K., Gordon, M.D., 1999. Literature-based discovery by lexical statistics, JASIST, 50 (7), 574-587.
  12. Marrs, K.A., Novak, G., 2004. Just-in-Time Teaching in Biology: Creating an Active Learner Classroom Using the Internet. Cell Biology Education, 3, 49-61.
  13. Morita, Y., Shingyoji, C., 2004. Effects of imposed bending on microtubule sliding in sperm flagella. Current Biology, 14(23), 2113-2118.
  14. Nakano, I., Kobayashi, T., Yoshimura, M., Shingyoji, C., 2003. Central-pair-linked regulation of microtubule sliding by calcium in flagellar axonemes. Journal of Cell Science, 116 (8), 1627-1636.
  15. Racunas, S.A., Shah, N.H., Albert, I., Fedoroff, N.V., 2004. HyBrow: a prototype system for computer-aided hypothesis evaluation. Biofinformatics, 20 (Suppl 1), i257-i264.
  16. Sakakibara, H.M., Kunioka, Y., Yamada, T., Kamimura, S., 2004. Diameter oscillation of axonemes in seaurchin sperm flagella. Biophys J., 86(1 Pt 1), 346-352.
  17. Srinivasan, P., 2004. Text Mining: Generating Hypotheses From MEDLINE. JASIST, 55(5), 396-413.
  18. Swanson, D.R., 1986. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30, 7-18.
  19. Swanson, D.R., 1988. Migraine and Magnesium: Eleven neglected connections. Perspectives in Biology and Medicine, 31, 526-557.
  20. Swanson, D.R., 1990. Somatomedin C and Arginine: Implicit connections between mutually isolated literatures. Perspectives in Biology and Medicine, 33(2), 157-179.
  21. Swanson, D.R., Smalheiser, N.R., 1997. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelligence, 91, 183-203.
  22. Swanson, D. R., Smalheiser, N.R., Bookstein, A., 2001. Information discovery from complementary literatures: Categorizing viruses as potential weapons. JASIST, 52(10), 797-812.
  23. Weeber, M., Vos, R., Klein, H., de Jong-van den Berg, L.T.W, Aronson, A.R, Molema, G., 2003. Generating hypotheses by discovering implicit associations in the literature: A case report of a search for new potential therapeutic uses for thalidomide, In J. American Medical Informatics Association, 10(3), 252-259.
  24. Weeber, M., Kors, J.A., Mons, B., 2005. Online tools to support literature-based discovery in the life sciences. Briefings in Bioinformatics, 6(3), 277-286.
  25. Yanagisawa, H., Kamiya, R., 2004. A Tektin Homologues Is Decreased in Chlamydomonas Mutants Lacking an Axonemal Inner-Arm Dynein. Molecular Biology of the Cell, 15 (5), 2105-2115.

Paper Citation

in Harvard Style

Guo W. and Kraines S. (2009). DISCOVERING RELATIONSHIP ASSOCIATIONS IN LIFE SCIENCES USING ONTOLOGY AND INFERENCE . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 10-17. DOI: 10.5220/0002285300100017

in Bibtex Style

author={Weisen Guo and Steven B. Kraines},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
SN - 978-989-674-011-5
AU - Guo W.
AU - Kraines S.
PY - 2009
SP - 10
EP - 17
DO - 10.5220/0002285300100017