Abstract
In order to solve the problem of mining biomedical knowledge, a biomedical semantic-based knowledge discovery method (Bio-SKDM) is proposed. Using the semantic types and semantic relations of the biomedical concepts, Bio-SKDM can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The experiment result shows that compared with ARROWSMITH and LITLINKER, Bio-SKDM generates less but more relevant novel hypotheses and requires less human intervention in the discovery procedure.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Swanson D R. Fish-Oil, Raynaud’s Syndrome, and Undiscovered Public Knowledge[J]. Perspectives in Biology and Medicine, 1986, 30(1): 7–18.
Swanson D R. Undiscovered Public Knowledge[J]. Library Quarterly, 1986, 56(2):103–118.
Swanson D R. Two Medical Literatures that Are Logically But Not Bibliographically Connected[J]. Journal of the American Society for Information Science, 1987, 38(4): 228–233.
Swanson D R. A Second Example of Mutually Isolated Medical Literatures Related by Implicit, Unnoticed Connections[J]. Journal of the American Society for Information Science, 1989, 40(6): 432–435.
Swanson D R. Migraine And Magnesium: Eleven Neglected Connections[J]. Perspectives in Biology and Medicine, 1988, 31(4): 526–557.
Hristovski D, Stare J, Peterlin B, et al. Supporting Discovery in Medicine by Association Rule Mining in Medline and UMLS[J]. Medinfo, 2001, 10(2): 1344–1348.
Joshi R, Li X L, Ramachandaran S, et al. Automatic Model Structuring from Text Using Biomedical Ontology[C]// American Association for Artificial Intelligence (AAAI) Workshop. San Jose: AAAI Press, 2004: 74–79.
Lindsay R K, Gordon M D. Literature-Based Discovery by Lexical Statistics[J]. Journal of the American Society for Information Science, 1999, 50(7): 574–587.
Pratt W, Yetisgen-Yildiz M. Litlinker: Capturing Connections Across the Biomedical Literature[C]//Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island: ACM Press, 2003: 105–112.
Weeber M, Vos R, Klein H, et al. Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report for New Potential Therapeutic Uses for Thalidomide[J]. Journal of the American Medical Informatics Association, 2003, 10(3): 252–259.
Swanson D R, Smalheiser N R. An Interactive System for Finding Complementary Literatures: A Stimulus to Scientific Discovery[J]. Artificial Intelligence, 1997, 91(2): 183–203.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, G., Huang, C., Zhang, X. et al. A new method for mining biomedical knowledge using biomedical ontology. Wuhan Univ. J. Nat. Sci. 14, 134–136 (2009). https://doi.org/10.1007/s11859-009-0208-7
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11859-009-0208-7