[Purpose/significance] Relation prediction is an important task in knowledge graph completion, and plays an important role in improving the completeness of knowledge in knowledge graphs. The paper proposes a new relation prediction method that combines internal structure features and external text features, which aims to predict the missing relations between two entities in knowledge graphs. [Method/process] The method transforms the relation paths in a knowledge graph and the texts that involve entity relationships into matrixes, learns the structure features and text pattern features related to a specific relation type through convolutional neural networks, and then trains a model based on the learned features for relation prediction. [Result/conclusion] The results shows that the performance of our proposed method on evaluation data sets is superior to the state-of-the-art approaches, and the method can effectively improves the performance of knowledge graph relationship prediction. Through practical application, it wvas found that this method has high application value in knowledge services.
Lin Zefei
,
Ou Shiyan
. Research on Relation Prediction in Knowledge Graphs by Fusing Structure and Text Features[J]. Library and Information Service, 2020
, 64(21)
: 99
-110
.
DOI: 10.13266/j.issn.0252-3116.2020.21.013
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