Knowledge Verification From Data

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 1 - 15
• Main Authors: Wang, Xiangyu, Ban, Taiyu, Chen, Lyuzhou, Wu, Xingyu, Lyu, Derui, Chen, Huanhuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognition; Correlation; Iterative methods; Knowledge; Knowledge based systems; Knowledge engineering; Knowledge graph (KG); knowledge management; knowledge quality (KQ); Knowledge representation; Management methods; multisources; Quality management; Robustness (mathematics); Task analysis; Temperature distribution; Verification
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3202244

Knowledge verification is an important task in the quality management of knowledge graphs (KGs). Knowledge is a summary of facts and events based on human cognition and experience. Due to the nature of knowledge, most knowledge quality (KQ) management methods are designed by human experts or the characteristics of existing knowledge, which may be limited by human cognition and the quality of existing knowledge. Numerical data contain a wealth of potential information that may be helpful in verifying knowledge, which is rarely explored. However, due to the implicit representation of numerical data to facts as well as the noise in the data, it is challenging to use data to verify the knowledge. Therefore, this article proposes a knowledge verification method, which discovers the correlation and causality from numerical data to validate knowledge and then evaluate the quality of knowledge. Moreover, to address the impact of noise, the method integrates multisource knowledge to jointly evaluate the KQ. Specifically, an iterative update method is designed to update KQ by utilizing the consistency between multisource knowledge while designing knowledge verification factors based on data causality and correlation to manage update process. The method is validated with multiple datasets, and the results demonstrate that the proposed method could evaluate KQ more accurately and has strong robustness to noise in the data.