Oct 17, 2021 · We propose a novel graph convolutional network (GCN)-based approach for interpretable inductive reasoning with relational path contrast, named RPC-IR.

A novel graph convolutional network (GCN)-based approach for interpretable inductive reasoning with relational path contrast, named RPC-IR, which achieves ...

Sep 10, 2024 · Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is ...

We aim to solve two main issues in this task. To acquire the entity independence semantics from first-order logic rules, LogCo extracts relational paths in each.

Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning · no code implementations • 17 Oct 2021 • Yudai Pan, Jun Liu ...

In order to deal with potential new entities, the key to inductive relation prediction is to use information irrelevant to specific entities. The most ...

Jul 7, 2022 · In this viedo, we talk about the model ConGLR that incorporates context graph with logical reasoning for inductive relation prediction. ConGLR ...

Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning ... relations in incomplete triples, whereas the dominant ...

Learning first-order rules with relational path contrast for inductive relation reasoning. Y Pan, J Liu, L Zhang, X Hu, T Zhao, Q Lin. arXiv preprint arXiv: ...

Sep 8, 2024 · This paper proposes a novel method that captures both connections between entities and the intrinsic nature of entities, by simultaneously ...