mlsanityData to 14 Oct 2026

From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies

arXiv 2506.09566 ↗First posted 11 Jun 2025cs.CL cs.AI cs.LGcited by 3

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) enhances factual grounding and reasoning capabilities. This survey paper systematically examines the synergy between KGs and LLMs, categorizing existing approaches into two main groups: KG-enhanced LLMs, which improve reasoning, reduce hallucinations, and enable complex question answering; and LLM-augmented KGs, which facilitate KG construction, completion, and querying. Through comprehensive analysis, we identify critical gaps and highlight the mutual benefits of structured knowledge integration. Compared to existing surveys, our study uniquely emphasizes scalability, computational efficiency, and data quality. Finally, we propose future research directions, including neuro-symbolic integration, dynamic KG updating, data reliability, and ethical considerations, paving the way for intelligent systems capable of managing more complex real-world knowledge tasks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 30 of its 65 references that are in the map. First posted on arXiv 11 Jun 2025.

Builds on

30 of 65 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains24 Oct
  2. 2024Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs31 Jul
  3. 2024Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs30 Jun
  4. 2024Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering3 Jun
  5. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities22 May

Other references · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  6. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  7. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  8. 2023A Survey of Large Language Modelscited by 2,576
  9. 2019Language Models as Knowledge Bases?cited by 1,936
  10. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  11. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  12. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
13 more
  1. 2020Knowledge Graphscited by 621
  2. 2019K-BERT: Enabling Language Representation with Knowledge Graphcited by 304
  3. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  4. 2022A Survey of Knowledge Enhanced Pre-trained Language Modelscited by 76
  5. 2024CARTE: Pretraining and Transfer for Tabular Learningcited by 76
  6. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  7. 2020PairRE: Knowledge Graph Embeddings via Paired Relation Vectorscited by 67
  8. 2023Making Large Language Models Perform Better in Knowledge Graph Completioncited by 48
  9. 2020Neural Entity Linking: A Survey of Models Based on Deep Learningcited by 46
  10. 2024SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphscited by 31
  11. 2024Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphscited by 24
  12. 2024Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answeringcited by 20
  13. 2021Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensemblescited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thoughtcited by 0
  2. 2025AI Consciousness and Existential Riskcited by 0
  3. 2026Diagnosing Structural Failures in LLM-Based Evidence Extraction for Meta-Analysiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Injecting Knowledge Graphs into Large Language Models
  2. 2024Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data
  3. 2024Synergizing Knowledge Graphs with Large Language Models: A Comprehensive Review and Future Prospects ↗
  4. 2023Large Knowledge Model: Perspectives and Challenges
  5. 2024Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering
  6. 2023Rethinking Language Models as Symbolic Knowledge Graphs
  7. 2024Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
  8. 2025Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs