Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning

Xiaohua Hu, Illhoi Yoo, Min Song, Yanqing Zhang, Il Yeol Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Two complementary and non-interactive literature sets of articles, when they are considered together, can reveal useful information of scientific interest not apparent in either of the two document sets. Swanson called the existence of such knowledge, undiscovered public knowledge (UDPK). This paper proposes a semantic-based mining model for UDPK. Our method replaces manual ad-hoc pruning with using semantic knowledge from the biomedical ontologies. Using the semantic types and semantic relationships of the biomedical concepts, our prototype system can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The system successfully replicates Swanson's two famous discoveries: Raynaud disease/fish oils and migraine/magnesium. Compared with previous approaches, our methods generate much fewer but more relevant novel hypotheses, and require much less human intervention in the discovery procedure.

Original languageEnglish
Title of host publicationCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages249-250
Number of pages2
ISBN (Print)1595931406, 9781595931405
DOIs
Publication statusPublished - 2005
EventCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management - Bremen, Germany
Duration: 2005 Oct 312005 Nov 5

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
CountryGermany
CityBremen
Period05/10/3105/11/5

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning'. Together they form a unique fingerprint.

  • Cite this

    Hu, X., Yoo, I., Song, M., Zhang, Y., & Song, I. Y. (2005). Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning. In CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management (pp. 249-250). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/1099554.1099611