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

• URL: http://arxiv.org/abs/2506.09566v1
• Date: Wed, 11 Jun 2025 09:58:14 GMT
• Title: From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies
• Authors: Blaž Škrlj, Boshko Koloski, Senja Pollak, Nada Lavrač,
• Abstract summary: This survey paper systematically examines the synergy between Knowledge Graphs (KGs) and Large Language Models (LLMs)<n> KG-enhanced LLMs, which improve reasoning, reduce hallucinations, and enable complex question answering; and LLM-augmented KGs, which facilitate KG construction, completion, and querying.<n>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.
• Score: 4.118037156777793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Knowledge Graph-extended Retrieval Augmented Generation for Question Answering [10.49712834719005]
  This paper proposes a system that integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) without requiring training.<n>The resulting approach can be classified as a specific form of a Retrieval Augmented Generation (RAG) with a KG.<n>It includes a question decomposition module to enhance multi-hop information retrieval and answerability.
  arXiv  Detail & Related papers  (2025-04-11T18:03:02Z)
• Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation [52.8352968531863]
  Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks.<n>This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain.
  arXiv  Detail & Related papers  (2025-03-31T15:58:08Z)
• Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models [51.47994645529258]
  We propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance.<n> Experimental results demonstrate that QAP outperforms state-of-the-art methods across multiple datasets, highlighting its effectiveness.
  arXiv  Detail & Related papers  (2025-03-30T17:09:11Z)
• RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration [4.604003661048267]
  RAG-KG-IL is a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models.<n>It integrates Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) with an Incremental Learning (IL) approach.<n>We evaluate the framework using real-world case studies involving health-related queries.
  arXiv  Detail & Related papers  (2025-03-14T11:50:16Z)
• Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
  We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base.<n>We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability.<n>Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
  arXiv  Detail & Related papers  (2024-12-30T13:36:05Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Research Trends for the Interplay between Large Language Models and Knowledge Graphs [5.364370360239422]
  This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) It aims to address gaps in current research by exploring areas such as KG Question Answering, ontology generation, KG validation, and the enhancement of KG accuracy and consistency through LLMs.
  arXiv  Detail & Related papers  (2024-06-12T13:52:38Z)
• Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering [38.31983923708175]
  We propose Dual-Reasoning, a novel framework that integrates an external system based on Graph Neural Network (GNN) for explicit reasoning on Knowledge Graphs (KGs)<n>We show that DualR achieves state-of-the-art performance while maintaining high efficiency and interpretability.
  arXiv  Detail & Related papers  (2024-06-03T09:38:28Z)
• KG-RAG: Bridging the Gap Between Knowledge and Creativity [0.0]
  Large Language Model Agents (LMAs) face issues such as information hallucinations, catastrophic forgetting, and limitations in processing long contexts. This paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation) pipeline to enhance the knowledge capabilities of LMAs. Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable improvements in the reduction of hallucinated content.
  arXiv  Detail & Related papers  (2024-05-20T14:03:05Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• Federated Neural Graph Databases [53.03085605769093]
  We propose Federated Neural Graph Database (FedNGDB), a novel framework that enables reasoning over multi-source graph-based data while preserving privacy. Unlike existing methods, FedNGDB can handle complex graph structures and relationships, making it suitable for various downstream tasks.
  arXiv  Detail & Related papers  (2024-02-22T14:57:44Z)
• Complex Logical Reasoning over Knowledge Graphs using Large Language Models [13.594992599230277]
  Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the relationships between entities. Current approaches rely on learning geometries to embed entities in vector space for logical query operations. We propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning.
  arXiv  Detail & Related papers  (2023-05-02T02:21:49Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.