Rule-Based Entity Resolution on Database with Hidden Temporal Information (Extended Abstract)

In this paper, we deal with the problem of rule-based entity resolution on imprecise temporal data. We use record matching dependencies and data currency constraints to derive temporal records' information and trend of their attributes' evolvement with elapsing of time. We firstly block records into smaller blocks, and then by exploring data currency constraints. We propose a temporal clustering approach with two steps, i.e., the skeleton clustering and the banding clustering. Experiments show that our method achieves both high accuracy and efficiency with hidden temporal information on datasets without imprecise timestamps.