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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD (2008)
Chu, X., Morcos, J., Ilyas, I.F., Ouzzani, M., Papotti, P., Tang, N., Ye, Y.: KATARA: a data cleaning system powered by knowledge bases and crowdsourcing. In: SIGMOD (2015)
Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell. 194, 28–61 (2013)
Hua, M., Pei, J.: DiMaC: a system for cleaning disguised missing data. In: SIGMOD (2008)
Lakshminarayan, K., Harp, S.A., Goldman, R.P., Samad, T.: Imputation of missing data using machine learning techniques. In: KDD (1996)
Mayfield, C., Neville, J., Prabhakar, S.: ERACER: a database approach for statistical inference and data cleaning. In: SIGMOD (2010)
Yang, K., Li, J., Wang, C.: Missing values estimation in microarray data with partial least squares regression. In: Alexandrov, V.N., Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3992, pp. 662–669. Springer, Heidelberg (2006). doi:10.1007/11758525_90
Acknowledgement
This paper was partially supported by NSFC grant U1509216, 61472099, National Sci-Tech Support Plan 2015BAH10F01, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Provience LC2016026 and MOE - Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology. Hongzhi Wang is the corresponding author of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Qi, Z., Wang, H., Meng, F., Li, J., Gao, H. (2017). Capture Missing Values with Inference on Knowledge Base. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-55705-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55704-5
Online ISBN: 978-3-319-55705-2
eBook Packages: Computer ScienceComputer Science (R0)