Research on Knowledge Base Error Detection Method Based on Confidence Learning

被引：0
|
作者：
Li W. [1 ,2 ]
Zhang Z. [1 ,2 ,3 ]
机构：
[1] National Science Library, Chinese Academy of Sciences, Beijing
[2] Department of Library, Information and Archives Mangement, School of Economic and Management, University of Chinese Academy of Sciences, Beijing
[3] Hubei Key Laboratory of Big Data in Science and Technology, Wuhan
来源：
Data Analysis and Knowledge Discovery | 2021年 / 5卷 / 09期
关键词：
Confidence Learning; Error Detection; Knowledge Base;
D O I：
10.11925/infotech.2096-3467.2021.0179
中图分类号：
学科分类号：
摘要：
[Objective] This paper explores the error detection method for knowledge base with the help of confidence learning, aiming to reduce the noise data. [Methods] We used the TransE model to represent knowledge base triples, and used the multi-layer perceptron model to detect errors. Then, we cleaned the dataset with confidence learning, and reduced the influence of noise data through multiple rounds of iterative training. [Results] We examined our new method with DBpedia datasets, and found the optimal F1 value reached 0.736 4, which is better than the control group. [Limitations] The noise data in the experiment was artificially generated and was different from the distribution of real world data. More research is needed to evaluate our method with larger knowledge bases. [Conclusions] The proposed method could reduce the influence of noise data through confidence learning, and more effectively detect knowledge base errors. © 2023 Chin J Gen Pract. All rights reserved.
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页码：1 / 9
页数：8
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