@inproceedings{d1cd57d439924032b4df781da2409aea,
title = "Capture missing values with inference on knowledge base",
abstract = "Data imputation is a basic step for data cleaning. Traditional data imputation approaches are lack of accuracy in the absence of knowledge. Involving knowledge base in imputation could overcome this shortcoming. A challenge is that the missing value could be hardly found directly in the knowledge bases (KBs). To use knowledge base sufficiently for imputation, we present FOKES, an inference algorithm on knowledge bases. The inference not only makes full use of true facts in KBs, but also utilizes types to ensure the accuracy of captured missing values. Extensive experiments show that our proposed algorithm can capture missing values efficiently and effectively.",
keywords = "Data quality, Imputation, Inference, Knowledge base, Missing values",
author = "Zhixin Qi and Hongzhi Wang and Fanshan Meng and Jianzhong Li and Hong Gao",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; International Workshops on Database Systems for Advanced Applications, DASFAA 2017, 4th International Workshop on Big Data Management and Service, BDMS 2017, 2nd Workshop on Big Data Quality Management, BDQM 2017, 4th International Workshop on Semantic Computing and Personalization, SeCoP 2017, 1st International Workshop on Data Management and Mining on MOOCs, DMMOOC 2017 ; Conference date: 27-03-2017 Through 30-03-2017",
year = "2017",
doi = "10.1007/978-3-319-55705-2\_14",
language = "英语",
isbn = "9783319557045",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "185--194",
editor = "Lijun Chang and Goce Trajcevski and Wen Hua and Zhifeng Bao",
booktitle = "Database Systems for Advanced Applications - DASFAA 2017 International Workshops",
address = "德国",
}