title: MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge.
abstract: 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.
authors.affiliation: Department of Bio and Brain Engineering, KAIST, South Korea.
authors.forename: Ali Z
authors.initials: AZ
authors.lastname: Ijaz
authors.forename: Min
authors.initials: M
authors.lastname: Song
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007255:Information Services; D016239:MEDLINE; D008962:Models, Theoretical; D009323:Natural Language Processing; D017432:Unified Medical Language System
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