Abstract
Given a relational table, we study the problem of detecting and repairing erroneous data, as well as marking correct data, using well curated knowledge bases (KBs). We propose detective rules (DRs), a new type of data cleaning rules that can make actionable decisions on relational data, by building connections between a relation and a KB. The main invention is that a DR simultaneously models two opposite semantics of an attribute belonging to a relation using types and relationships in a KB: The positive semantics explains how its value should be linked to other attribute values in a correct tuple, and the negative semantics indicate how a wrong attribute value is connected to other correct attribute values within the same tuple. Naturally, a DR can mark correct values in a tuple if it matches the positive semantics. Meanwhile, a DR can detect/repair an error if it matches the negative semantics. We study fundamental problems associated with DRs, e.g., rule consistency and rule implication. We present efficient algorithms to apply DRs to clean a relation, based on rule order selection and inverted indexes. Moreover, we discuss approaches on how to generate DRs from examples. Extensive experiments, using both real-world and synthetic datasets, verify the effectiveness and efficiency of applying DRs in practice.
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