PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion

arXiv:2609.02216 · cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/yychi-code/PEARL

Terminology

• Contextual Subgraph — 47× in this paper
• Knowledge Graph — 44× in this paper · explained in 7 episodes
• Embeddings — 25× in this paper · explained in 2 episodes
• Contrastive Learning — 17× in this paper · explained in 5 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Knowledge graphs — 9× in this paper · explained in 2 episodes
• Subgraph Contrastive Learning — 9× in this paper
• Core — 8× in this paper · explained in 2 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• LLM-based Path Retrieval and Alignment — 5× in this paper
• Aggregation — 4× in this paper · explained in 1 episode
• Bipartite Graph Neural Network — 4× in this paper
• Relation-Aware Path Attention Fusion — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Unified Framework — 4× in this paper · explained in 5 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Contextual Subgraph Neural Network — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Path-Entity Bipartite Graph — 2× in this paper
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Knowledge Graph Completion (KGC) — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mean Reciprocal Rank — 1× in this paper
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Composition-based Multi-Relational Graph Convolutional Networks
• MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction
• Qwen3 Technical Report

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