中圖分類號： TP183
文獻標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.10.005
引用格式： 繆琦，楊昕悅. 具有關(guān)系敏感嵌入的知識庫錯誤檢測[J].信息技術(shù)與網(wǎng)絡(luò)安全，2020，39(10)：23-27，37.

Knowledge base error detection with relation sensitive embedding

Abstract： Accuracy and quality are very important for the knowledge base. Although there have been many researches on the incompleteness of knowledge base, few workers consider the detection of errors in the knowledge base. According to the traditional methods, it is usually unable to effectively capture the internal correlation of errors in the knowledge base, so as to check the errors. In this paper, a relational sensitive embedded method NSIL for knowledge base is proposed to obtain the correlation among the relationships between them, so as to check out the errors in the knowledge base, so as to improve the accuracy and quality of the knowledge base. This method is divided into two stages: correlation processing and error detection. In the correlation processing stage, correlation function of NSIL is used to obtain the correlation degree of each relationship in the form of score; in the error detection stage, error detection is based on the score of correlation degree, and missing component prediction is carried out for the triplet of missing subject or object. At last, the method is verified on the benchmark data set "FB15K" which is generated by Freebase, one of the largest knowledge bases. It is proved that the method has high performance in knowledge base error detection.

Key words : knowledge base；embedding model；error detection