MKEM: A multi-level knowledge emergence model for mining undiscovered public knowledge

Document Type

Conference Proceeding

Publication Date

12-1-2009

Abstract

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 hypothesesand expand knowledge. In this paper, 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. 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. Copyright 2009 ACM.

Identifier

74049100158 (Scopus)

ISBN

[9781605588032]

Publication Title

International Conference on Information and Knowledge Management Proceedings

External Full Text Location

https://doi.org/10.1145/1651318.1651329

First Page

51

Last Page

58

Grant

0434581

Fund Ref

National Science Foundation

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