Modeling entity evolution for temporal record matching

Abstract

Temporal record matching recognizes that if the entities represented by the records change over time, approaches that use temporal information may do better than approaches that do not. Any such temporal matching method relies at its heart on a temporal model that captures information about how entities evolve. In their pioneering work, Li {\it et al.} used an efficiently computable model that simply tries to predict if an attribute is expected to change over a given time interval. In our work, we propose and evaluate a more detailed model that focuses on the probability that a given attribute value reappears over time. The intuition here is that an entity might change its attribute value in the way that is dependent on its past values. In addition, our model considers sets of records (rather than simply pairs of records) to improve robustness and accuracy. Experimental results show that the resulting approach improves both accuracy and resistance to noise while incurring a minimal overhead.

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