Journal article

Approximate Subgraph Matching-Based Literature Mining for Biomedical Events and Relations

Haibin Liu, Lawrence Hunter, Vlado Keselj, Karin Verspoor

PLoS One | PUBLIC LIBRARY SCIENCE | Published : 2013


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..

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Awarded by National Library of Medicine Informatics Training grant

Awarded by National Institutes of Health


Funding Acknowledgements

This research was supported by the National Library of Medicine Informatics Training grant 5T15LM009451, and National Institutes of Health grants 5R01LM009254 and 5R01LM008111. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This research was supported in part by the Intramural Research Program of the NIH, NLM.