Reasoning about record matching rules

To accurately match records it is often necessary to utilize the se-mantics of the data. Functional dependencies (FDs) have proven useful in identifying tuples in a clean relation, based on the se-mantics of the data. For all the reasons that FDs and their infer-ence are needed, it is also important to develop dependencies and their reasoning techniques for matching tuples from unreliable data sources. This paper investigates dependencies and their reasoning for record matching. (a) We introduce a class of matching depen-dencies (MDs) for specifying the semantics of data in unreliable re-lations, defined in terms of similarity metrics and a dynamic seman-tics. (b) We identify a special case of MDs, referred to as relative candidate keys (RCKs), to determine what attributes to compare and how to compare them when matching records across possibly dif-ferent relations. (c) We propose a mechanism for inferring MDs, a departure from traditional implication analysis, such that when we cannot match records by comparing attributes that contain errors, we may still find matches by using other, more reliable attributes. (d) We provide an O(n2) time algorithm for inferring MDs, and an effective algorithm for deducing a set of RCKs from MDs. (e) We experimentally verify that the algorithms help matching tools efficiently identify keys at compile time for matching, blocking or windowing, and that the techniques effectively improve both the quality and efficiency of various record matching methods. 1