A Labeled Graph Kernel for Relationship Extraction

Gonçalo Simões; Helena Galhardas; David Matos

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arXiv · 1302.4874

A Labeled Graph Kernel for Relationship Extraction

Gonçalo Simões·Helena Galhardas·David Matos

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Abstract

In this paper, we propose an approach for Relationship Extraction (RE) based on labeled graph kernels. The kernel we propose is a particularization of a random walk kernel that exploits two properties previously studied in the RE literature: (i) the words between the candidate entities or connecting them in a syntactic representation are particularly likely to carry information regarding the relationship; and (ii) combining information from distinct sources in a kernel may help the RE system make better decisions. We performed experiments on a dataset of protein-protein interactions and the results show that our approach obtains effectiveness values that are comparable with the state-of-the art kernel methods. Moreover, our approach is able to outperform the state-of-the-art kernels when combined with other kernel methods.

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Gonçalo Simões, Helena Galhardas, David Matos. 2013-02-20. A Labeled Graph Kernel for Relationship Extraction. https://arxiv.org/abs/1302.4874

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