Knowledge Graph Completion Method of Combining Structural Information with Semantic Information  

作　　者：Binhao HU Jianpeng ZHANG Hongchang CHEN 

机构地区：[1]School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China [2]National Digital Switching System Engineering&Technological R&D Center(NDSC),Zhengzhou 450002,China

出　　处：《Chinese Journal of Electronics》2024年第6期1412-1420,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.62002384);the General Project of China Postdoctoral Science Foundation(Grant No.2020M683760);the Songshan Laboratory Project(Grant No.221100210700-03)。

摘　　要：With the development of knowledge graphs,a series of applications based on knowledge graphs have emerged.The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs.To improve the quality of knowledge graphs,translation-based graph embeddings such as TransE,learn structural information by representing triples as low-dimensional dense vectors.However,it is difficult to generalize to the unseen entities that are not observed during training but appear during testing.Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples.Although they are robust to incompleteness,they need to calculate the score of all candidate entities for each triple during inference.We consider combining two models to enhance the robustness of unseen entities by semantic information,and prevent combined explosion by reducing inference overhead through structured information.We use a pre-training language model to code triples and learn the semantic information within them,and use a hyperbolic space-based distance model to learn structural information,then integrate the two types of information together.We evaluate our model by performing link prediction experiments on standard datasets.The experimental results show that our model achieves better performances than state-of-the-art methods on two standard datasets.

关 键 词：Knowledge graph Knowledge graph completion Representation learning 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]