• biological event extraction;
  • biological information extraction;
  • subgraph matching;
  • subgraph isomorphism

An important task in biological information extraction is to identify descriptions of biological relations and events involving genes or proteins. In this work, we propose a graph-based approach to automatically learn rules for detecting biological events in the life science literature. The event rules are learned by identifying the key contextual dependencies from full parsing of annotated text. The detection is performed by searching for isomorphism between event rules and the dependency graphs of complete sentences. When applying our approach to the data sets of the Task 1 of the BioNLP-ST 2009, we achieved a 40.71% F-score in detecting biological events across nine event types. Our 56.32% precision is comparable with the state-of-the-art systems. The approach may also be generalized to extract events from other domains where training data are available because it requires neither manual intervention nor external domain-specific resources. The subgraph matching algorithm we developed is released under the new BSD license and can be downloaded from