# Inductive Relation Prediction

11 papers with code • 0 benchmarks • 0 datasets

Inductive setting of the knowledge graph completion task. This requires a model to perform link prediction on an entirely new test graph with new set of entities.

## Benchmarks

These leaderboards are used to track progress in Inductive Relation Prediction
## Most implemented papers

# Inductive Relation Prediction by Subgraph Reasoning

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

# Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction

To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.

# Communicative Message Passing for Inductive Relation Reasoning

Relation prediction for knowledge graphs aims at predicting missing relationships between entities.

# Inductive Relation Prediction by BERT

Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting.

# Cycle Representation Learning for Inductive Relation Prediction

In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology.

# Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.

# Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer

Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions.

# Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers

Relation prediction on knowledge graphs (KGs) is a key research topic.

# Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference

In this work, we bridge the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models by introducing a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting.

# Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

To address this challenge, we propose Anchoring Path Sentence Transformer (APST) by introducing Anchoring Paths (APs) to alleviate the reliance of CPs.