Abstract
Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric HRR is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets.
Funder
Anhui Provincial Natural Science Foundation;Independent Scientific Research Program of National University of Defense Science and Technology
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference37 articles.
1. Dbpedia: A nucleus for a web of open data;Auer,2007
2. YAGO: A Large Ontology from Wikipedia and WordNet
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献