Entity Resolution in Sparse Encounter Network Using Markov Logic Network

Abstract

Entity Resolution, which identifies different descriptions referring to the same real-world entity, is a fundamental stage in data integration process essential for quality data analysis. Identities recognition is important in encounter network as it defines the entities of encounters. It is usually not a problem if unique identifier information, e.g., mobile phone number, is available. However, in the circumstances where unique identifier is not available or in question, further investigated is required to perform the entity resolution on the encounter dataset. Often the encounter network is a sparse network with very limited information collected from close-range person-to-person contact reporting, as in epidemiology contact tracing or traffic collision reports. In this paper, we provide an automatic method to resolve the ambiguity of entities in sparse encounter network. We develop a Bayesian spatiotemporal inference system to infer the probability of entity's visits on places of interest. Then, we propose a hierarchical Markov logic network to tackle the inference of the entities in the network which analyses the connection strength of network with multiple types of entities. Experimental results on encounter networks of synthetic and commercial traffic encounter datasets demonstrate that the proposed method achieves better accuracy than existing collective classifications.