Knowledge Tree
multi-hop reasoning
Facts (34)
Sources
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 7 facts
referenceKnowledge reasoning tasks are divided into single-hop prediction and multi-hop reasoning (Ren et al. 2022).
claimSingle-hop prediction predicts one element of a triplet for given two elements, whereas multi-hop reasoning predicts one or more elements in a multi-hop logical query.
referenceMulti-hop reasoning on massive knowledge graphs is a challenging task (Zhu et al. 2022) because most existing studies focus on smaller graphs with only 63K entities and 592K relations.
claimMulti-hop reasoning achieves more precise formation of triplets compared to single-hop prediction, making it a critical need for the development of knowledge graphs.
referenceRen et al. (2022) published 'Smore: Knowledge graph completion and multi-hop reasoning in massive knowledge graphs' in the Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, which addresses the challenges of knowledge graph completion and reasoning at scale.
referenceMulti-hop reasoning requires traversing multiple relations and intermediate entities, which can lead to exponential computation costs (Zhang et al. 2021).
claimExisting multi-hop reasoning models cannot effectively learn from training sets for massive knowledge graphs containing millions of entities.
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org Jun 29, 2025 4 facts
measurementKetotifen eye drops (antihistamines) and Fluorometholone eye drops are used together for managing eye allergies, and LLM-based multi-hop reasoning for this ophthalmological condition achieved 93% accuracy.
measurementIn the second phase of MKG evaluation, expert LLMs achieved an 89% accuracy rate when answering complex medical queries requiring multi-hop reasoning, such as managing comorbidities or determining multi-drug treatment protocols.
claimThe Medical Knowledge Graph (MKG) evaluation criteria included assessing the correctness and completeness of medical relationships, the validity of multi-hop reasoning paths, and the utility of the graph in real-world medical applications like diagnostic and treatment decision-making.
measurementAllergic interstitial nephritis can lead to renal papillary necrosis, and LLM-based multi-hop reasoning for this clinical progression achieved 91% accuracy and a 9.0/10 relevance rating.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 3 facts
referenceThe HippoRAG method (Gutiérrez et al., 2024) identifies relevant knowledge graph subgraphs by integrating multi-hop reasoning with single-step multi-hop knowledge retrieval.
claimIncorporating knowledge graphs with LLMs enables multi-hop and iterative reasoning over factual knowledge graphs, which augments the reasoning capability of LLMs for complex question answering.
claimRAG-based question answering systems face three primary technical challenges: (1) knowledge conflicts arising from inconsistent or overlapping data between LLMs and external sources, (2) poor relevance and quality of retrieved context which directly impacts answer accuracy, and (3) a lack of iterative and multi-hop reasoning capabilities required for questions needing global or summarized contexts.
Combining Knowledge Graphs With LLMs | Complete Guide - Atlan atlan.com Jan 28, 2026 3 facts
claimGraphRAG traverses knowledge graph relationships to gather connected context, enabling multi-hop reasoning, whereas traditional RAG retrieves text chunks based on semantic similarity without understanding how information connects.
claimGraphRAG implementations demonstrate substantial improvements for multi-hop reasoning compared to simple vector retrieval.
claimMulti-hop reasoning in knowledge graph-augmented LLM architectures enables answering questions that require linking information across several related entities.
Knowledge Graphs vs RAG: When to Use Each for AI in 2026 - Atlan atlan.com Feb 12, 2026 3 facts
claimKnowledge graphs enable multi-hop reasoning by allowing AI to follow relationship chains, such as healthcare systems connecting symptoms to diseases, treatments, and patient demographics.
claimKnowledge graphs support multi-hop reasoning and complex path finding, whereas RAG systems are limited to single-step similarity matching.
claimResearch indicates that context graphs incorporating relationships significantly improve performance on multi-hop reasoning tasks compared to flat retrieval methods.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
claimReLMKG (Cao and Liu, 2023) struggles with dynamic multi-hop reasoning and lacks interpretability.
referenceKG-Agent, proposed by Jiang J. et al. in 2024, utilizes programming languages to design multi-hop reasoning processes on knowledge graphs and synthesizes code-based instruction datasets for fine-tuning base LLMs.
referenceLKPNR (Runfeng et al., 2023) combines multi-hop reasoning across knowledge graphs with LLM context understanding.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 2 facts
claimRecent advances in neuro-symbolic AI aim to mitigate scalability and performance issues through modular and hierarchical designs, approximate symbolic inference, and scalable neural backends like graph neural networks (GNNs) that support multi-hop reasoning.
referenceA differentiable neuro-symbolic reasoning approach for large knowledge graphs, introduced in reference [126], leverages logical rules for multi-hop reasoning while using neural embeddings to generalize and handle scale.
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org Dec 2, 2025 1 fact
claimIn specialized domains such as law, medicine, and science, text generation by Large Language Models (LLMs) often suffers from a lack of coherence and logical consistency, particularly when tasks require multi-hop reasoning and analysis.
Knowledge Graphs and GenAI: When the Complexity Is Worth It medium.com Oct 1, 2025 1 fact
claimKnowledge graphs excel at multi-hop reasoning and explainability.
How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Jun 18, 2025 1 fact
claimConstructing a knowledge graph from documents enables multi-hop reasoning by making it easier to traverse and navigate interconnected documents to answer complex queries.
LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph puppygraph.com Feb 19, 2026 1 fact
perspectiveThe LLM knowledge graph architecture is a necessary evolution that addresses the risks of purely parametric AI systems by using graph structures for verifiable grounding, deterministic multi-hop reasoning, and explicit traceability, thereby solving the 'last mile' problem of enterprise AI by translating raw language capability into reliable business intelligence.
How NebulaGraph Fusion GraphRAG Bridges the Gap Between ... nebula-graph.io Jan 27, 2026 1 fact
claimGraphRAG improves contextual relevance, enables multi-hop reasoning, and provides inherent explainability by allowing conclusions to be traced back through a path of nodes and relationships.
Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org Aug 7, 2025 1 fact
claimGraph-based RAG (GraphRAG) addresses the limitations of traditional RAG by constructing a structured knowledge graph from a source corpus to enable semantically aware retrieval and multi-hop reasoning.
A Knowledge Graph-Based Hallucination Benchmark for Evaluating ... arxiv.org Feb 23, 2026 1 fact
referenceHotpotQA, as described by Yang et al. (2018), introduces complexity to QA benchmarks by requiring multi-hop reasoning to arrive at an answer.
Survey and analysis of hallucinations in large language models frontiersin.org Sep 29, 2025 1 fact
claimLeast-to-Most prompting (Zhou et al., 2022) mitigates hallucination in multi-hop reasoning tasks by decomposing complex queries into simpler steps.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 1 fact
claimComplexWebQuestions is a benchmark for evaluating complex question answering over knowledge graphs by testing a model's ability to handle multi-hop reasoning and compositional questions.