Differentiable learning of rules with constants in knowledge graph

Knowledge reasoning helps overcome the incompleteness of knowledge graphs (KGs) and has significantly contributed to the development of large KGs. Rule mining, one of the key tasks of knowledge reasoning, studies the problem of learning interpretable inference patterns over KGs. Existing rule mining...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Knowledge-based systems 2023-09, Vol.275, p.110686, Article 110686
Hauptverfasser: Xu, Zezhong, Ye, Peng, Li, Juan, Chen, Huajun, Zhang, Wen
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Knowledge reasoning helps overcome the incompleteness of knowledge graphs (KGs) and has significantly contributed to the development of large KGs. Rule mining, one of the key tasks of knowledge reasoning, studies the problem of learning interpretable inference patterns over KGs. Existing rule mining methods mainly focus on learning rules that consist of different relations and variables, restricting the form of rules to be closed path. While rules could be diverse and in order to enrich the forms of rules, we argue that constants should also be considered in the rule mining process. We propose an Elegant Differentiable rUle learning with Constant mEthod (EduCe).11Source code of EduCeis available at https://github.com/yep96/Educe. We propose a constant operator and dynamic weight mechanism, which choose the constants that should be added and decrease the number of parameters, respectively. The model could mine diverse and accurate rules in an efficient way with these modules. The experimental results on several knowledge graph completion benchmarks show that EduCe achieves state-of-the-art link prediction results among differentiable rule mining methods and successfully learns diverse and high-quality rules. •Draw attention to expanding the diversity of target rules for rule mining and emphasize the importance of constants to rules.•Propose an end-to-end differentiable rule mining method that can mine rules in an efficient way.•Experimentally demonstrate the importance of rule with constants.•Design an algorithm that outputs high-quality symbolic rules with constants.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2023.110686