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SimCat: an entity similarity measure for heterogeneous knowledge graph with categories

Establishing similarity between heterogeneous entities in a complex knowledge graph is a challenging task due to the unrestricted nature of categories and relation types. In large graphs, the semantic roles of relation types and entity categories are strongly interdependent at a statistical scale and help in defining a key component of entity similarity measures. In this paper we define a measure that incorporates category information as well as aggregated relationship graph structures. Experimental evaluation in terms of entity retrieval performance on a large subset of the Freebase graph establishes the efficacy of the system.