Fast and Slow Thinking: A Two-Step Schema-Aware Approach for Instance Completion in Knowledge Graphs

Modern Knowledge Graphs (KG) often suffer from an incompleteness issue (i.e., missing facts). By representing a fact as a triplet (h,r,t) linking two entities h and t via a relation r , existing KG completion approaches mostly consider a link prediction task to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (h,r,?) . However, this task implicitly has a strong yet impractical assumption on the two given elements in a triplet, which have to be correlated, resulting otherwise in meaningless predictions, such as ( Marie Curie , headquarters location , ?). Against this background, this paper studies an instance completion task suggesting r - t pairs for a given h , i.e., (h,?,?) . Inspired by the human psychological principle "fast-and-slow thinking", we propose a two-step schema-aware approach RETA++ to efficiently solve our instance completion problem. It consists of two components: a fast RETA-Filter efficiently filtering candidate r - t pairs schematically matching the given h , and a deliberate RETA-Grader leveraging a KG embedding model scoring each candidate r - t pair considering the plausibility of both the input triplet and its corresponding schema. RETA++ systematically integrates them by training RETA-Grader on the reduced solution space output by RETA-Filter via a customized negative sampling process, so as to fully benefit from the efficiency of RETA-Filter in solution space reduction and the deliberation of RETA-Grader in scoring candidate triplets. We evaluate our approach against a sizable collection of state-of-the-art techniques on three real-world KG datasets. Results show that RETA-Filter can efficiently reduce the solution space for the instance completion task, outperforming best baseline techniques by 10.61%-84.75% on the reduced solution space size, while also being 1.7x-29.6x faster than these techniques. Moreover, RETA-Grader trained on the reduced ...