Incorporating Term Definitions for Taxonomic Relation Identification

Taxonomic relations (also called “is-A” relations) are key components in taxonomies, semantic hierarchies and knowledge graphs. Previous works on identifying taxonomic relations are mostly based on linguistic and distributional approaches. However, these approaches are limited by the availability of a large enough corpus that can cover all terms of interest and provide sufficient contextual information to represent their meanings. Therefore, the generalization abilities of the approaches are far from satisfactory. In this paper, we propose a novel neural network model to enhance the semantic representations of term pairs by encoding their respective definitions for the purpose of taxonomic relation identification. This has two main benefits: (i) Definitional sentences represent specified corpus-independent meanings of terms, hence definition-driven approaches have a great generalization capability to identify unseen terms and taxonomic relations which are not expressed in domain specificity of the training data; (ii) Global contextual information from a large corpus and definitions in the sense level can provide richer interpretation of terms from a broader knowledge base perspective, and benefit the accurate prediction for the taxonomic relations of term pairs. The experimental results show that our model outperforms several competitive baseline methods in terms of F-score on both specific and open domain datasets.