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Knowledge Error Detection via Textual and Structural Joint Learning

Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450003, China, and with Key Laboratory of AI Safety, Chinese Academy of Sciences (CAS), Beijing 100190, China, and also with Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing 100190, China
Key Laboratory of AI Safety, CAS, Beijing 100190, China, and with Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing 100190, China
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Abstract

Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which can negatively impact the performance of downstream applications. Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. Therefore, this paper proposes a novel error detection framework that combines both structural and textual information. The framework utilizes a confidence module for error detection while generating knowledge embeddings. The performance of this approach outperforms baseline methods in error detection and link prediction experiments, particularly achieving state-of-the-art performance in the error detection task.

Keywords

Knowledge Graph (KG)error detectiontextual informationconfidence score

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Big Data Mining and Analytics
Volume 8 Issue 1,
February 2025
Pages 233-240
DOI: 10.26599/BDMA.2024.9020040
Cite this article:
Wang X, Ao X, Zhang F, et al. Knowledge Error Detection via Textual and Structural Joint Learning. Big Data Mining and Analytics, 2025, 8(1): 233-240. https://doi.org/10.26599/BDMA.2024.9020040
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