HRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion

Zongwei Liang,Hui Liu,Junan Yang,Lin Cui,Keju Huang,Xiang Li,Lingzhi Qu

doi:10.3390/electronics11060908

Zongwei Liang, Hui Liu + Show 5 more

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https://doi.org/10.3390/electronics11060908

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Journal: Electronics	Publication Date: Mar 15, 2022
Citations: 2	License type: CC BY 4.0

Affiliation: National University of Defense Technology

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.

Highlights

Large scale knowledge graphs (KGs) such as Freebase [1], DBpedia [2], NELL [3], and YAGO [4], have achieved significant development in recent years
In order to alleviate the above two drawbacks of the current logic-rule methods, this paper proposes HRER, a knowledge graph reasoning model based on the logic rule and entity rules
This paper proposes the new index—Horn rule reliability (HRR), which alleviates the problem caused by incompleteness and biased distribution

Summary

Introduction

Large scale knowledge graphs (KGs) such as Freebase [1], DBpedia [2], NELL [3], and YAGO [4], have achieved significant development in recent years. These KGs contain considerable facts stored in the form of triples (h, r, t), where h, r, t represents the heads, the relations, and the tails, respectively. Let E denote the set of all entities and R the set of all relations present in KGs. In the following, we utilize the notation (h, r, t) (head entity, relation, tail entity) to identify a triple in KG, with h, t ∈ E , r ∈ R denoting the subject(head) ,the object(tail) and the relation between them, respectively. A Horn rule consists of a head and a body, where the head is a single atom and the body is a set of atoms. The paper denotes rule with head r(x, y) and body {B1, . . . , Bn}: B1 ∧ B2 ∧ · · · ∧ Bn ⇒ r(x, y)

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