Fast and Slow Thinking: A Two-Step Schema-Aware Approach for Instance Completion in Knowledge Graphs
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(2024)
摘要
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 ...
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关键词
Task analysis,Tail,Predictive models,Knowledge graphs,Training,Data models,Psychology,Knowledge graph embedding,entity types,instance completion,fast and slow thinking
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