Abstract
Missing values are a data quality problem affecting almost every type of real world datasets. Since poor data quality has a direct impact on organisational success, there is a dire need to eradicate missing values as a way to minimise costs and increase efficiency in companies. There are different methods to deal with missing values including the Imputation Methods, which try to compute an accurate estimation of missing values using the rest of the information available. This article devises the potential of Semantic Technologies towards the solution of the limitations of current Imputation Methods by proposing alternative approaches.
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Acknowledgement
This work has received funding from ELKARTEK project KK-2020/00049 3KIA of the Basque Government, REACT project from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395, FEDER/TIN2016-78011-C4-2-R, TIN2016-78365-R (Spanish Ministry of Economy, Industry and Competitiveness) and IT-1244-19 (Basque Government). Unai Garciarena holds a predoctoral grant from the University of the Basque Country (ref. PIF16/238).
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Esnaola-Gonzalez, I., Garciarena, U., Bermúdez, J. (2021). Semantic Technologies Towards Missing Values Imputation. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_16
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