Knowledge Tree
knowledge graph reasoning
Also known as: knowledge graph-elicited reasoning
Facts (27)
Sources
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 9 facts
referenceChatKBQA (Luo H. et al., 2023) and RoG (Luo et al., 2023b) integrate knowledge graph reasoning into conversational question answering systems to enhance factual accuracy and discourse coherence.
referenceLuo et al. (2023a) proposed 'ChatRule', a method for mining logical rules with large language models for knowledge graph reasoning, in the preprint 'Chatrule: mining logical rules with large language models for knowledge graph reasoning'.
claimKnowledge Graph Reasoning (KGR) improves LLM reasoning processes by extracting claims from initial LLM drafts and performing a chain of verification using structured knowledge.
claimThe probabilistic nature of Large Language Models (LLMs) creates fundamental explainability barriers in knowledge graph reasoning tasks.
claimKnowledge Graph Reasoning (KGR) improves the reliability and relevance of LLM responses by autonomously integrating real-time knowledge from Knowledge Graphs.
claimKnowledge Graph Reasoning (KGR) ensures the alignment of Large Language Model (LLM) output with verified knowledge by cross-referencing the output with Knowledge Graph data.
claimKnowledge graph reasoning leverages graph structures and logical rules to infer new information or relationships from existing knowledge.
claimKnowledge Graph Reasoning (KGR) improves the coherence and accuracy of LLM output by ensuring contextually relevant facts are used during the generation process.
claimKnowledge Graph Reasoning (KGR) helps counterbalance biases in LLM training data by relying on Knowledge Graphs as an objective source of factual information.
Construction of intelligent decision support systems through ... - Nature nature.com Oct 10, 2025 7 facts
claimThe 'integration performance' dimension in the IKEDS evaluation framework is assessed by measuring cross-domain reasoning success, orchestration efficiency via pathway selection, and the alignment between knowledge graph reasoning and generative components.
referenceFan et al. authored 'MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot', published in the Proceedings of the ACM on Web Conference in 2025.
referenceThe KG-Only baseline utilizes conventional knowledge graph reasoning algorithms, such as graph traversal, logical inference, and constraint satisfaction, to produce entity recommendations without generative components.
procedureFor every decision task, the Dynamic Knowledge Orchestration Engine evaluates five potential pathways: execution of pure knowledge graph reasoning, execution of pure retrieval-augmented generation, sequential application of both, parallel application with fusion, and execution of iterative interaction with feedback loops.
claimMedRAG combines knowledge graph-elicited reasoning with retrieval-augmented generation for healthcare applications.
accountDuring the development of the Integrated Knowledge-Enhanced Decision Support framework, the orchestration strategy evolved to favor knowledge graph reasoning for structured queries and generative approaches for ambiguous queries.
referenceThe Parallel-KG-RAG baseline system used in the IKEDS framework evaluation runs knowledge graph reasoning and retrieval-augmented generation in parallel, combining outputs through a weighted ensemble method without the deep integration mechanisms found in the IKEDS framework.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 2 facts
claimXuejiao Zhao, Siyan Liu, Su-Yin Yang, and Chunyan Miao published the paper 'MedRAG: Enhancing retrieval-augmented generation with knowledge graph-elicited reasoning for healthcare copilot' in 2025.
claimODA (Sun et al., 2024b) incorporates knowledge graph reasoning capabilities through a global observation approach, which improves reasoning abilities by employing a cyclical paradigm of observation, action, and reflection.
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org Jun 29, 2025 2 facts
referenceMedical QA research has progressed through three complementary lines: domain-specific language models, retrieval-augmented generation (RAG), and knowledge-graph reasoning.
referenceXuejiao Zhao et al. (2025) published 'Medrag: Enhancing retrieval-augmented generation with knowledge graph-elicited reasoning for healthcare copilot' as an arXiv preprint (arXiv:2502.04413), which focuses on improving RAG with knowledge graphs.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 2 facts
claimXiong W, Hoang T, Wang WY published the paper 'Deep path: a reinforcement learning method for knowledge graph reasoning' as an arXiv preprint in 2017.
claimWan G, Pan S, Gong C et al published the paper 'Reasoning like human: hierarchical reinforcement learning for knowledge graph reasoning' in the Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence in 2021.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 2 facts
referenceGuanglin Niu, Bo Li, Yongfei Zhang, and Shiliang Pu created a closed-loop neural-symbolic learning framework for knowledge graph inference in 2021.
referenceHu et al. (2022b) proposed a method for empowering language models by integrating knowledge graph reasoning for question answering tasks.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 1 fact
claimGraph Neural Networks (GNNs) are used for tasks including link prediction, node classification, recommendation systems, and knowledge graph reasoning.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
referenceExisting survey papers on neuro-symbolic AI generally focus on broad overviews or specific applications, including cybersecurity, military operations, reinforcement learning, knowledge graph reasoning, and validation and verification.
LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com 1 fact
referenceThe paper 'MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot' (WWW, 2025) proposes using knowledge graph-elicited reasoning to enhance retrieval-augmented generation for healthcare applications.