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The relation triples extraction method based on table filling can address theissues of relation overlap and bias propagation. However, most of them onlyestablish separate table features for each relationship, which ignores theimplicit relationship between different entity pairs and different relationshipfeatures. Therefore, a feature reasoning relational triple extraction methodbased on table filling for technological patents is proposed to explore theintegration of entity recognition and entity relationship, and to extractentity relationship triples from multi-source scientific and technologicalpatents data. Compared with the previous methods, the method we proposed forrelational triple extraction has the following advantages: 1) The table fillingmethod that saves more running space enhances the speed and efficiency of themodel. 2) Based on the features of existing token pairs and table relations,reasoning the implicit relationship features, and improve the accuracy oftriple extraction. On five benchmark datasets, we evaluated the model wesuggested. The result suggest that our model is advanced and effective, and itperformed well on most of these datasets.

A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents