Entity Resolution in Sparse Encounter Network Using Markov Logic Network

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