Structural Constraints for Multipartite Entity Resolution with Markov Logic Network

Multipartite entity resolution seeks to match entity mentions across several collections. An entity mention is presumed unique within a collection, and thus could match at most one entity mention in each of the other collections. In addition to domain-specific features considered in entity resolution, there are a number of domain-invariant structural contraints that apply in this scenario, including one-to-one assignment as well as cross-collection transitivity. We propose a principled solution to the multipartite entity resolution problem, building on the foundation of Markov Logic Network (MLN) that combines probabilistic graphical model and first-order logic. We describe how the domain-invariant structural constraints could be expressed appropriately in terms of Markov logic, flexibly allowing joint modeling with domain-specific features. Experiments on two real-life datasets, each spanning four collections, show the utility of this approach and validate the contributions of various MLN components.