基于解析图嵌入和加权图卷积网络的知识图谱补全

Meiqiu Luo, Chunxia Zhang*, Cheng Peng, Xin Zhang, Guisuo Guo, Zhendong Niu

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

Knowledge graph completion is an important research issue in knowledge graph construction, knowledge engineering, and natural language processing. A knowledge graph is a knowledge support for realizing accurate knowledge services in general and professional fields. It is also an important breakthrough foundation in information retrieval, question-and-answer interactions, and information recommendation. The low quality and small scale of the knowledge graph are the main bottlenecks that hinder its wide applications. The purpose of knowledge graph completion is to build a large-scale and high-quality knowledge graph for continuously updating and expanding the knowledge graph. Aiming at the difficulty of knowledge graph completion methods to extract deep semantic features from auxiliary information, such as unstructured texts, this study proposes a knowledge graph completion method based on parsing graph embedding and weighted graph convolutional network. This method uses the weighted graph convolutional network to model the semantic dependency parsing of the entity description and construct the semantic dependency parsing graph embedding. Furthermore, it introduces a multi-grained sentence-embedding generation method of the entity description, which is intended to build entity representation learning that can capture multi-grained semantics and deep-level semantic features. The experimental results on two public datasets show that the proposed knowledge graph completion approach outperforms the existing methods, thereby demonstrating its effectiveness and superiority.

投稿的翻译标题Knowledge graph completion based on parsing graph embedding and a weighted graph convolutional network
源语言繁体中文
页(从-至)2037-2057
页数21
期刊Scientia Sinica Informationis
52
11
DOI
出版状态已出版 - 2022

关键词

  • entity representation learning
  • knowledge graph completion
  • parsing graph embedding
  • semantic dependency parsing
  • weighted graph convolutional network

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引用此

Luo, M., Zhang, C., Peng, C., Zhang, X., Guo, G., & Niu, Z. (2022). 基于解析图嵌入和加权图卷积网络的知识图谱补全. Scientia Sinica Informationis, 52(11), 2037-2057. https://doi.org/10.1360/SSI-2021-0217