Differentiable Learning of Rules with Constants in Knowledge Graph
Knowledge reasoning, helping overcome the incompleteness issue of knowledge graph(KG), significantly contributes to the development of large KG, which consists of relations and constants. Rule mining studies the problem of capturing interpretable patterns over KG, which is one of the key tasks of knowledge reasoning. However, previous works mainly focus on the combination of different relations, and are limited for ignoring the importance of constants. In this paper, we propose that constants should be considered in rule mining process, and introduce an Elegant Differentiable rUle learning with Constant mEthod (EduCe). Based on soft constant operator and dynamic weight, the model we proposed can mine more diverse and accurate logical rules while controlling the number of parameters, which is also a great challenge to this problem. Experiment results on several benchmark datasets demonstrate the effectiveness and accuracy of rule with constants.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule …
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsDifferentiable Learning of Graph-like Logical Rules from Knowledge Graphs
Logical rules inside a knowledge graph (KG) are essential for reasoning, logical inference, and rule mining. However, existing works can only handle simple, i.e., chain-like and tree-like, rules and cannot capture KG's c…
Knowledge GraphsDifferentiable learning of numerical rules in knowledge graphs
Rules over a knowledge graph (KG) capture interpretable patterns in data and can be used for KG cleaning and completion. Inspired by the TensorLog differentiable logic framework, which compiles rule inference into a sequ…
Knowledge GraphsGLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning
Differentiable inductive logic programming (ILP) techniques have proven effective at finding approximate rule-based solutions to link prediction and node classification problems on knowledge graphs; however, the common a…
Inductive logic programmingKnowledge Graph CompletionNode ClassificationKnowledge GraphsLogical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. …
Knowledge Graph CompletionKnowledge GraphsLink Prediction