MKEM: A Multi-level Knowledge Emergence Model for mining undiscovered public knowledge

Ali Z. Ijaz, Min Song, Doheon Lee

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Background: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.Methods: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.Results: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses.Conclusions: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. .

Original languageEnglish
Article numberS3
JournalBMC bioinformatics
Issue numberSUPPL. 2
Publication statusPublished - 2010 Apr 16

Bibliographical note

Funding Information:
This work was supported by National Research Lab Program (R0A-2005-000-10094-0) from the Ministry of Education, Science and Technology, and the Information Technology Research Center (grant IITA-2008-C1090-0801-0001). DL was also supported by the Korea Institute of Science and Technology Information. MS was supported by the National Science Foundation under grant DUE-0434581, by the Institute for Museum and Library Services under grant LG-02-04-0002-04, and by the New Jersey Institute of Technology. This article has been published as part of BMC Bioinformatics Volume 11 Supplement 2, 2010: Third International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2009. The full contents of the supplement are available online at

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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