Approximate subgraph matching-based literature mining for biomedical events and relations.

ABSTRACT: The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it

SUBMITTER: Liu H 

PROVIDER: S-EPMC3629260 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature