Inferring Binary Relation Schemas for Open Information Extraction

Kangqi Luo, Xusheng Luo, Kenny Qili Zhu

Abstract: This paper presents a framework to model the semantic representation of binary relations produced by open information extraction systems. For each binary relation, we infer a set of preferred types on the two arguments simultaneously, and generate a ranked list of type pairs which we call schemas. All inferred types are drawn from the Freebase type taxonomy, which are human readable. Our system collects 171,168 binary relations from ReVerb, and is able to produce top-ranking relation schemas with a mean reciprocal rank of 0.337.