An Inductive Logical Model with Exceptional Information for Error Detection and Correction in Large Knowledge Bases

Yan Wu (), Xiao Lin, Haojie Lian and Zili Zhang
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Yan Wu: Department of Foundational Courses Dujiangyan Campus, Sichuan Agricultural University, Chengdu 611830, China
Xiao Lin: The York Management School, University of York, York YO10 5DD, UK
Haojie Lian: Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
Zili Zhang: College of Computer and Information Science, Southwest University, Chongqing 400715, China

Mathematics, 2025, vol. 13, issue 11, 1-23

Abstract: Some knowledge bases (KBs) extracted from Wikipedia articles can achieve very high average precision values (over 95% in DBpedia). However, subtle mistakes including inconsistencies, outliers, and erroneous relations are usually ignored in the construction of KBs by extraction rules. Automatic detection and correction of these subtle errors is important for improving the quality of KBs. In this paper, an inductive logic programming with exceptional information (EILP) is proposed to automatically detect errors in large knowledge bases (KBs). EILP leverages the exceptional information problems that are ignored in conventional rule-learning algorithms such as inductive logic programming (ILP). Furthermore, an inductive logical correction method with exceptional features (EILC) is proposed to automatically correct these mistakes by learning a set of correction rules with exceptional features, in which respective metrics are provided to validate the revised triples. The experimental results demonstrate the effectiveness of EILP and EILC in detecting and repairing large knowledge bases, respectively.

Keywords: rule learning; exceptional information; correction rules; inductive logic programming (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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