Inferring Binary Relation Schemas for Open Information Extraction
Kangqi Luo, Xusheng Luo, Kenny Qili Zhu · 2015
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 Re-Verb, and is able to produce top-ranking relation schemas with a mean reciprocal rank of 0.337.