Context Similarity for Retrieval-Based Imputation

Abstract

Completeness as one of the four major dimensions of data quality is a pervasive issue in modern databases. Although data imputation has been studied extensively in the literature, most of the research is focused on inference-based approach. We propose to harness Web tables as an external data source to effectively and efficiently retrieve missing data while taking into account the inherent uncertainty and lack of veracity that they contain. Existing approaches mostly rely on standard retrieval techniques and out-of-the-box matching methods which result in a very low precision, especially when dealing with numerical data. We, therefore, propose a novel data imputation approach by applying numerical context similarity measures which results in a significant increase in the precision of the imputation procedure, by ensuring that the imputed values are of the same domain and magnitude as the local values, thus resulting in an accurate imputation. We use Dresden Web Table Corpus which is comprised of more than 125 million web tables extracted from the Common Crawl as our knowledge source. The comprehensive experimental results demonstrate that the proposed method well outperforms the default out-of-the-box retrieval approach.