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Exploiting graph kernels for high performance biomedical relation extraction.

Nagesh C Panyam1, Karin Verspoor2, Trevor Cohn1

  • 1School of Computing and Information Systems, University of Melbourne, Melbourne, Australia.

Journal of Biomedical Semantics
|February 1, 2018
PubMed
Summary
This summary is machine-generated.

This study demonstrates that graph kernels, specifically the All Path Graph (APG) kernel, achieve high performance in extracting Chemical-Induced Disease (CID) relations from biomedical text. Graph kernels significantly outperform tree kernels for this task, even when relations span multiple sentences.

Keywords:
APG kernelASM kernelGraph kernelsRelation extraction

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Relation extraction from biomedical text is crucial for semantic mining.
  • Kernel methods, particularly graph kernels, are effective for complex structures like enhanced dependency parse graphs.
  • Existing methods often rely on manual feature engineering or task-specific rules.

Purpose of the Study:

  • To develop and evaluate a high-performance Chemical-Induced Disease (CID) relation extraction system.
  • To compare the effectiveness of various kernel methods (tree vs. graph) for biomedical relation extraction.
  • To investigate the application of graph kernels for relations spanning multiple sentences.

Main Methods:

  • Comparative study of tree kernels (Subset Tree Kernel, Partial Tree Kernel) and graph kernels (All Path Graph (APG) kernel, Approximate Subgraph Matching (ASM) kernel).
  • Development of novel modifications to the ASM kernel to improve performance.
  • Application of graph kernels for extracting relations across multiple sentences.

Main Results:

  • The developed CID relation extraction system achieved an F-score of 60%, outperforming the state-of-the-art (56% without rules).
  • Graph kernels substantially outperformed tree kernels for the CID task, with the APG kernel achieving the best performance (60% F-score).
  • The APG kernel generally outperformed the ASM kernel across various datasets for Protein-Protein Interaction (PPI) extraction, though ASM showed promise on specific datasets.

Conclusions:

  • Graph kernels are highly effective for biomedical relation extraction, including CID and Protein-Protein Interaction (PPI) tasks.
  • The APG kernel demonstrated superior performance compared to the ASM kernel and tree kernels across most evaluated datasets.
  • This work highlights the potential of graph kernels for extracting complex relations, even those distributed across multiple sentences, without external knowledge or heuristics.