On Existential First Order Queries Inference on Knowledge Graphs

Abstract

Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one. Specifically, answering first-order logic formulas is of particular interest because of its clear syntax and semantics. Recently, the query embedding method has been proposed which learns the embedding of a set of entities and treats logic operations as set operations. Though there has been much research following the same methodology, it lacks a systematic inspection from the standpoint of logic. In this paper, we characterize the scope of queries investigated previously and precisely identify the gap between it and the whole family of existential formulas. Moreover, we develop a new dataset containing ten new formulas and discuss the new challenges coming simultaneously. Finally, we propose a new search algorithm from fuzzy logic theory which is capable of solving new formulas and outperforming the previous methods in existing formulas