| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
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| Challenge: | Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens. |
| Approach: | They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability. |
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Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
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| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
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Multi-Hop Knowledge Graph Reasoning with Reward Shaping (D18-1)
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| Challenge: | Multi-hop reasoning is an effective approach for query answering over incomplete knowledge graphs (KGs). |
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A Collaborative Reasoning Framework Powered by Reinforcement Learning and Large Language Models for Complex Questions Answering over Knowledge Graph (2025.coling-main)
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| Challenge: | Knowledge Graph Question Answering (KGQA) aims to answer natural language questions by reasoning across multiple triples in knowledge graphs. |
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Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature. |
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GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)
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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui
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Learning Contextualized Knowledge Structures for Commonsense Reasoning (2021.findings-acl)
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Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
| Challenge: | Recent knowledge graph (KG) augmented models have achieved notable success on commonsense reasoning tasks. |
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Correcting on Graph: Faithful Semantic Parsing over Knowledge Graphs with Large Language Models (2025.findings-acl)
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| Challenge: | Complex multi-hop questions require comprehensive retrieval and reasoning. |
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