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

Authors:
Ali Zeeshan Ijaz;Min Song;Doheon Lee
Affiliations:
KAIST, Daejeon, South Korea;NJIT, New Jersey, USA;KAIST, Daejeon, South Korea
Venue:
Proceedings of the third international workshop on Data and text mining in bioinformatics
Year:
2009

Citing 9
Cited 1

Toward discovery support systems: a replication, re-examination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil

Journal of the American Society for Information Science
Fast discovery of association rules

Advances in knowledge discovery and data mining
Using latent semantic indexing for literature based discovery

Journal of the American Society for Information Science
Literature-based discovery by lexical statistics

Journal of the American Society for Information Science
LitLinker: capturing connections across the biomedical literature

Proceedings of the 2nd international conference on Knowledge capture
Text mining: generating hypotheses from MEDLINE

Journal of the American Society for Information Science and Technology
The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text

Journal of Biomedical Informatics - Special issue: Unified medical language system
Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning

Proceedings of the 14th ACM international conference on Information and knowledge management
Discovering Novel Causal Patterns From Biomedical Natural-Language Texts Using Bayesian Nets

IEEE Transactions on Information Technology in Biomedicine

TNMCA: generation and application of network motif based inference models for drug repositioning

Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics

Quantified Score

Hi-index	0.00

Visualization

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.