Authors:
Weisen Guo
and
Steven B. Kraines
Affiliation:
The University of Tokyo, Japan
Keyword(s):
Relationship Associations, Association Rules, Semantic Relationships, Semantic Matching, Semantic Web, Ontology, Logical Inference, Life Sciences, Literature-based Knowledge Discovery
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Over one million papers are published annually in life sciences. Bioinformatics and knowledge discovery fields aim to help researchers conduct scientific discovery using the existing published knowledge. Existing literature-based discovery methods and tools mainly use text-mining techniques to extract non-specified relationships between two concepts. We present an approach that uses semantic web techniques to measure the relevance between two relationships with specified types that involve a particular entity. We consider two highly relevant relationships as a relationship association. Relationship associations could help researchers generate scientific hypotheses or create computer-interpretable semantic descriptors for their papers. The relationship association extraction process is described and the results of experiments for extracting relationship associations from 392 semantic graphs representing MEDLINE papers are presented