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DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

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arxiv 1707.06690 v3 pith:HZ3VNFBX submitted 2017-07-20 cs.CL cs.AI

Wenhan Xiong , Thien Hoang , William Yang Wang This is my paper · ORCID
classification cs.CLcs.AI
keywords learningknowledgegraphmethodreinforcementaccuracyagentalgorithm
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We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Combining graph wavelet embeddings with Neural Bellman-Ford message passing reduces the layers needed for inductive logical query answering on large knowledge graphs.

  2. Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning

    cs.AI 2026-03 conditional novelty 6.0 of 10

    Expert explanation preferences can be captured by two LLM-generated personas and used as reinforcement-learning rewards to produce adaptive knowledge-graph explanations that experts prefer over generic ones.

  3. Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs

    cs.AI 2025-07 conditional novelty 6.0 of 10

    Context Pooling improves inductive link prediction in knowledge graphs by building a query-specific subgraph that keeps only neighbors whose relation types co-occur with the query relation.

  4. Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    MERRY integrates graph structure and entity/relation text via multi-perspective message passing, improving zero-shot knowledge graph completion and question answering over strong baselines.

  5. Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion

    cs.CL 2025-06 reject novelty 5.0 of 10

    A relation-prediction model that combines top-K edge message passing with a conditional flow matching auxiliary loss, reporting near-perfect relation prediction and a 25% relative MRR gain in entity prediction.

  6. Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE

    cs.AI 2025-09 reject novelty 4.0 of 10

    KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.

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