Slicing through the Scientific Literature

  • Christopher J. O. Baker
  • Patrick Lambrix
  • Jonas Laurila Bergman
  • Rajaraman Kanagasabai
  • Wee Tiong Ang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5647)


Success in the life sciences depends on access to information in knowlegde bases and literature. Finding and extracting the relevant information depends on a user’s domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant queries in response to keyword input from the user. Queries are presented in natural language, translated to formal query syntax and issued to a knowledge base of scientific literature, documents or aligned document segments. We describe the components of the system and exemplify using real-world examples.


Text Mining Query Term Keyword Query Text Segment Query Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ang, W.T., Kanagasabai, R., Baker, C.J.O.: Knowledge translation: Computing the query potential of bioontologies. In: International Workshop on Semantic Web Applications and Tools for Life Sciences (2008)Google Scholar
  2. 2.
    Baker, C.J.O., Kanagasabai, R., Ang, W.T., Veeramani, A., Low, H.S., Wenk, M.R.: Towards ontology-driven navigation of the lipid bibliosphere. BMC Bioinformatics 9(suppl. 1), S5 (2008)CrossRefGoogle Scholar
  3. 3.
    Bontcheva, K., Davis, B.: Natural language generation from ontologies. In: Davis, Grobelnik, Mladenic (eds.) Semantic Knowledge Management, Integrating Ontology Management, Knowledge Discovery, and Human Language Technologies, pp. 113–127. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    Doms, A., Schroeder, M.: GoPubMed - exploring PubMed with the gene ontology. Nucleic Acids Research 33(web server issue), W783–W786 (2005)CrossRefGoogle Scholar
  5. 5.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)Google Scholar
  6. 6.
    Fontelo, P., Liu, F., Ackerman, M.: askMEDLINE: a free-text, natural language query tool for MEDLINE/PubMed. BMC Medical Informatics and Decision Making 5(1) (2005)Google Scholar
  7. 7.
    Haarslev, V., Möller, R., Wessel, M.: Querying the semantic web with Racer + nRQL. In: Proceedings of the International Workshop on Applications of Description Logics (2004)Google Scholar
  8. 8.
    Harris, M.D.: Building a large-scale commercial nlg system for an EMR. In: Proceedings of the Fifth International Natural Language Generation Conference (2008)Google Scholar
  9. 9.
    Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review 18(1), 1–31 (2003)CrossRefGoogle Scholar
  10. 10.
    Kanagasabai, R., Low, H.-S., Ang, W.T., Wenk, M.R., Baker, C.J.O.: Ontology-centric navigation of pathway information mined from text. In: Knowledge in Biology - 11th Annual Bio-Ontologies Meeting colocated with Intelligent Systems for Molecular Biology, pp. 1–4 (2008)Google Scholar
  11. 11.
    Lambrix, P., Tan, H.: SAMBO - a system for aligning and merging biomedical ontologies. Journal of Web Semantics 4(3), 196–206 (2006)CrossRefGoogle Scholar
  12. 12.
    Lambrix, P., Tan, H., Jakonienė, V., Strömbäck, L.: Biological ontologies. In: Baker, Cheung (eds.) Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences, pp. 85–99. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Müller, H.-M., Kenny, E.E., Sternberg, P.W.: Textpresso: An ontology-based information retrieval and extraction system for biological literature. PLoS Biology 2(11), e309 (2004)CrossRefGoogle Scholar
  14. 14.
    Noy, N.F.: Semantic integration: A survey of ontology-based approaches. Sigmod Record 33(4), 65–70 (2004)CrossRefGoogle Scholar
  15. 15.
    Noy, N.F., Griffith, N., Musen, M.: Collecting community-based mappings in an ontology repository. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 371–386. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., Stoehr, P.: EBIMed - text crunching to gather facts for proteins from Medline. Bioinformatics 23, e237–e244 (2007)CrossRefGoogle Scholar
  17. 17.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christopher J. O. Baker
    • 1
  • Patrick Lambrix
    • 2
  • Jonas Laurila Bergman
    • 2
  • Rajaraman Kanagasabai
    • 3
  • Wee Tiong Ang
    • 3
  1. 1.Department of Computer Science & Applied StatisticsUniversity of New BrunswickCanada
  2. 2.Department of Computer and Information ScienceLinköpings universitetSweden
  3. 3.Data Mining Department, Institute for Infocomm ResearchAgency for Science Technology and ResearchSingapore

Personalised recommendations