xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem

Abstract

In this paper, we propose a global, selfexplainable solution to solve a prominent NLP problem: Entity Resolution (ER). We formulate ER as a graph partitioning problem. Every mention of a real-world entity is represented by a node in the graph, and the pairwise similarity scores between the mentions are used to associate these nodes to exactly one clique, which represents a real-world entity in the ER domain. In this paper, we use Clique Partitioning Problem (CPP), which is an Integer Program (IP) to formulate ER as a graph partitioning problem and then highlight the explainable nature of this method. Since CPP is NP-Hard, we introduce an efficient solution procedure, the xER algorithm, to solve CPP as a combination of finding maximal cliques in the graph and then performing generalized set packing using a novel formulation. We discuss the advantages of using xER over the traditional methods and provide the computational experiments and results of applying this method to ER data sets.

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

“…ExplainER [26] Model-specific Emboot [144] ProtoRE [24] MGNN [17], CRIAGE [72], ITransF [126], DensE [56], HopfE [9], METransE [118] Global self-explaining xER [108] FTL-LM [53],…”