Katara: A data cleaning system powered by knowledge bases and crowdsourcing

Classical approaches to clean data have relied on using integrity constraints, statistics, or machine learning. These approaches are known to be limited in the cleaning accuracy, which can usually be improved by consulting master data and involving experts to resolve ambiguity. The advent of knowledge bases (kbs), both general-purpose and within enterprises, and crowdsourcing marketplaces are providing yet more opportunities to achieve higher accuracy at a larger scale. We propose Katara, a knowledge base and crowd powered data cleaning system that, given a table, a kb, and a crowd, interprets table semantics to align it with the kb, identifies correct and incorrect data, and generates top-k possible repairs for incorrect data. Experiments show that Katara can be applied to various datasets and kbs, and can efficiently annotate data and suggest possible repairs.