Neuro-Symbolic Integration: Enhancing LLMs with Knowledge Graphs

Abstract

Large Language Models (LLMs) have revolutionized natural language processing, achieving remarkable success in tasks like text generation and question answering. However, their reasoning capabilities are constrained by hallucinations—generating plausible but factually incorrect outputs—and limited parametric memory, which hampers their ability to maintain context over long interactions or perform complex multi-step reasoning. This article synthesizes insights from 2024-2025 surveys on neuro-symbolic artificial intelligence, focusing on integrating LLMs with Knowledge Graphs (KGs) to enhance factual grounding, reasoning, and knowledge management in real-world applications. We explore methodologies for knowledge extraction, representation, reasoning, and dynamic updating, emphasizing bidirectional synergies where LLMs automate KG construction and KGs improve LLM reasoning. Empirical evidence highlights LLMs' strengths as inference assistants, with critical discussions addressing challenges like data incompleteness, bias propagation, and scalability. We propose future directions, including multi-modal reasoning with semantic segmentation, and introduce two research initiatives: one to mitigate hallucinations in limited-context scenarios using dynamic KGs, and another leveraging semantic segmentation, LLMs, and KGs for enhanced multi-modal reasoning in medical diagnostics. This synthesis aims to guide the development of robust, generalizable AI systems for complex reasoning tasks across diverse domains.

Keywords

Large Language Models (LLMs), Knowledge Graphs (KGs), Neuro-Symbolic AI, Retrieval-Augmented Generation (RAG), Semantic Segmentation, Reasoning Capabilities, Knowledge Extraction, Hallucination Mitigation, Scalability, Ethical AI, Multi-Modal Reasoning, Real-World Applications

Introduction

The advent of Large Language Models (LLMs) such as GPT-4, LLaMA, Claude, and PaLM has ushered in a new era of artificial intelligence, fundamentally reshaping natural language processing (NLP) by enabling unprecedented performance in tasks ranging from conversational dialogue to automated content generation and complex task-solving across domains like healthcare, finance, education, legal analysis, and scientific research [1][2]. These models, built on transformer architectures and trained on vast, diverse text corpora, leverage massive parameter counts—often in the billions—to capture intricate linguistic patterns, achieving near-human fluency in generating coherent and contextually relevant text. However, despite their remarkable capabilities, LLMs face significant challenges that limit their reliability in knowledge-intensive scenarios. Hallucinations, where models produce plausible but factually incorrect outputs, undermine trustworthiness, particularly in critical applications like medical diagnosis or legal reasoning [3] [4]. Limited context windows restrict their ability to maintain coherence in extended interactions, such as long conversational threads or multi-document analysis, often capping at a few thousand tokens. Additionally, LLMs lack explicit reasoning mechanisms for complex, multi-hop tasks, such as answering questions requiring inference across multiple facts (e.g., "What is the capital city of the country where the Eiffel Tower is located?"), which demand structured knowledge and logical deduction [5][6].

The historical trajectory of AI research provides context for addressing these limitations, reflecting a pendulum swing between symbolic and neural paradigms. In the 1970s and 1980s, symbolic systems like MYCIN and other rule-based expert systems dominated, offering interpretable, logic-driven reasoning for domain-specific tasks but struggling with scalability, brittleness, and the inability to process unstructured data like text or images [7]. The deep learning revolution of the 2010s shifted focus to neural approaches, with models like BERT and GPT leveraging data-driven learning to generalize across diverse tasks, but at the cost of interpretability and factual grounding. Neuro-symbolic AI has emerged as a hybrid paradigm to bridge these gaps, combining the inductive learning of neural networks with the deductive rigor of symbolic representations to create systems that are both flexible and precise [8]. Knowledge Graphs (KGs), which represent real-world entities (e.g., "Paris") and their relationships (e.g., "capital_of France") as interconnected nodes and edges, serve as a cornerstone for this integration, providing a structured, interpretable framework to ground LLMs in factual knowledge and enable multi-hop reasoning [9]. Surveys from 2024-2025 highlight the bidirectional synergies: LLMs automate KG construction by extracting entities and relations from unstructured sources like scientific literature, social media, or even visual data via semantic segmentation, while KGs enhance LLM reasoning by offering verifiable facts and supporting complex inference tasks [10], [11].

The significance of this integration is amplified in dynamic, real-world environments where knowledge evolves rapidly, such as in medical research, where new findings emerge daily, or in financial markets, where real-time data drives decisions. Neuro-symbolic systems enable dynamic KG updates without requiring full model retraining, ensuring adaptability and scalability [12]. Moreover, the incorporation of multi-modal approaches, such as combining KGs with semantic segmentation and Retrieval-Augmented Generation (RAG), extends these capabilities to tasks involving visual and textual data, such as visual question answering (VQA), autonomous navigation, and medical image analysis [13]. For instance, semantic segmentation can extract entities like tumors from medical scans, which are then integrated into KGs for reasoning alongside textual clinical reports, enhancing diagnostic accuracy [14]. This article synthesizes these advancements, drawing on recent surveys to explore methodological insights, practical applications, and two proposed research initiatives: one addressing hallucination mitigation in limited-context conversations using dynamic KGs, and another leveraging semantic segmentation, LLMs, and KGs for multi-modal reasoning in medical diagnostics. By critically evaluating integration paradigms, incorporating visual representations of key trends, and charting future directions, we aim to contribute to the development of ethical, robust AI systems that approach Artificial General Intelligence (AGI), capable of handling complex, real-world reasoning tasks with precision, reliability, and societal benefit.

Figure 1: Growth of Neuro-Symbolic AI Publications (2020-2024)

This chart illustrates the increasing research interest in neuro-symbolic AI, based on publication counts from 2020 to 2024, as reported in a systematic review. Data points include 53 publications in 2020, 80 in 2021, 120 in 2022, and 280 in 2023, with an estimated 300 in 2024 based on continued growth trends. Note that direct links to figures from academic sources are unavailable, as they are embedded in documents like PDFs on arXiv.

Figure 2: Taxonomy of Neuro-Symbolic AI Research Areas

This chart categorizes neuro-symbolic AI research areas based on a 2024 systematic review, showing the distribution of efforts in learning and inference (63%), knowledge representation (44%), logic and reasoning (35%), explainability and trustworthiness (28%), and meta-cognition (5%). Direct links to figures from academic sources are unavailable, as they are embedded in documents like PDFs on arXiv.

Figure 3: Taxonomy of Neuro-Symbolic AI

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Methodology

This synthesis is grounded in a systematic, multi-faceted approach to reviewing and analyzing recent literature, focusing on surveys and empirical studies from 2024-2025 to capture the latest advancements in neuro-symbolic AI. The primary objective is to distill actionable insights into how LLMs, integrated with KGs and multi-modal techniques like semantic segmentation, can enhance reasoning capabilities, mitigate hallucinations, and support real-world applications through effective knowledge extraction, representation, reasoning, and updating. By leveraging a structured methodology, we aim to provide a comprehensive taxonomy of integration paradigms, critically compare their strengths and limitations, and propose future research directions that address current challenges and capitalize on emerging opportunities, including multi-modal reasoning with visual and textual data.

Paper Filtering and Selection

The literature review began with a targeted search on arXiv.org, IEEE Xplore, and Google Scholar, using precise queries such as "survey on large language models and knowledge graphs 2024 2025," "neuro-symbolic AI survey LLM 2024 2025," "LLM knowledge representation reasoning," and "multi-modal reasoning with semantic segmentation 2024 2025." These queries were designed to capture advancements in LLM-KG integrations, neuro-symbolic frameworks, and multi-modal applications involving semantic segmentation for visual data processing. Inclusion criteria prioritized: (1) recency, requiring papers to be published or updated between January 2024 and August 2025; (2) relevance, focusing on surveys or studies addressing LLM-KG synergies, neuro-symbolic reasoning, or multi-modal applications with semantic segmentation; (3) comprehensiveness, favoring surveys covering multiple methodologies, benchmarks, or applications; and (4) impact, selecting papers with novel contributions or high citation potential based on early metrics or author reputation. Exclusion criteria eliminated non-English papers, non-validated preprints lacking empirical evidence, and studies focused solely on neural, symbolic, or unimodal methods without hybrid or multi-modal elements. From an initial pool of over 80 papers, 20 core surveys and studies were selected for in-depth analysis, supplemented by application-specific papers to provide practical context.

Objectives and Outcomes

The methodology pursued four primary objectives: (1) evaluating knowledge extraction techniques, such as entity and relation identification from unstructured text (e.g., news articles, scientific papers) or visual data via semantic segmentation (e.g., identifying objects in medical images); (2) analyzing knowledge representation methods, including graph embeddings, triple-based structures, and multi-modal representations integrating visual and textual data; (3) assessing reasoning capabilities, such as multi-hop question answering, link prediction, and visual reasoning in multi-modal contexts; and (4) exploring dynamic updating mechanisms to maintain KG relevance in evolving data environments, such as real-time medical diagnostics or autonomous systems. Expected outcomes included identifying strengths like hallucination reduction, improved reasoning accuracy, and enhanced multi-modal integration, alongside challenges such as computational overhead, bias propagation, and scalability limitations. Empirical evaluations used benchmarks like WikiKG90Mv2 for link prediction, FB15k-237 for relation completion, VINE for virtual knowledge extraction, and COCO for semantic segmentation tasks, with metrics including F1-score, Hits@K, exact match (EM), and mean Intersection over Union (mIoU). Multi-agent frameworks like AutoKG and multi-modal systems integrating semantic segmentation were analyzed for automation and robustness.

Methodological Details

The selected surveys employed systematic reviews, taxonomic categorizations, and empirical comparisons across diverse datasets. For example, experiments tested LLMs like GPT-4 and LLaMA on zero-shot, one-shot, and few-shot prompting for tasks like entity extraction (e.g., identifying "aspirin" as a drug in medical texts) and relation extraction (e.g., linking "aspirin" to "treats headache") across datasets like Re-TACRED and SciERC. Multi-modal studies incorporated semantic segmentation models (e.g., U-Net) to extract entities from images, such as tumors in medical scans, integrating these with KGs for reasoning. To address incomplete knowledge, surveys generated datasets with missing triples, ensuring alternative reasoning paths (e.g., inferring "Paris is in France" from related facts). Integration paradigms were classified into loose coupling (e.g., RAG querying KGs) and tight coupling (e.g., embedding-based ToG), with performance compared on large-scale KGs like Wikidata5M and multi-modal datasets like COCO. Reproducibility was ensured through open-source repositories on platforms like GitHub, providing access to code, datasets, and evaluation protocols. These methodologies highlight LLMs' strengths in reasoning over extraction, particularly in noisy or multi-modal datasets, and underscore the role of semantic segmentation in enriching KGs with visual data.

Summary Table of Surveys

Survey Title	Authors/Publication Date	Focus	Key Methodologies	Main Contributions/Outcomes
Neuro-Symbolic AI in 2024: A Systematic Review	Various / Jan 2025	Neuro-symbolic landscapes	Systematic review, taxonomy	Maps hybrid architectures, proposes LLM-SS framework. [15]
LLMs Meet KGs for Question Answering	Zhu et al. / May 2025	LLM-KG for QA	Comparative analysis, dataset testing	Shows high accuracy in multi-hop QA, proposes AutoKG. [16]
LLM-Enhanced Knowledge Representation Learning	Various / Jul 2024 (updated 2025)	LLM-KRL integration	Encoder/decoder reviews	Improves embeddings for reasoning tasks. [17]
KGs, LLMs, and Hallucinations	Various / Nov 2024	Hallucination mitigation	Dataset/method reviews	Reduces errors via KG grounding. [18]
Combining KGs and LLMs	Various / Jul 2024	Hybrid methods	Review of 28 papers	Taxonomizes approaches, highlights synergies. [19]

Related Works

The integration of LLMs with KGs builds on a rich history of AI research, combining neural and symbolic paradigms to address their respective limitations. This section reviews foundational and recent works, organized by key themes, to contextualize advancements in neuro-symbolic AI and multi-modal reasoning, including the role of semantic segmentation.

Neuro-Symbolic Paradigms

Neuro-symbolic AI merges neural pattern recognition with symbolic logical reasoning. Early systems like AlphaGo combined neural policy networks with symbolic Monte Carlo tree search to achieve superhuman performance in games. Recent advancements, such as AlphaGeometry, address data scarcity through symbolic proof generation, while LogicLM uses symbolic solvers to correct LLM errors, enhancing reliability in tasks like function verification [20].

Figure 4: Neuro-Symbolic Paradigms

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External Knowledge Integration

External knowledge enhances LLMs via unstructured (e.g., RAG) and structured sources (tables, KGs). Table-based reasoning includes symbolic (Text-to-SQL), neural (Chain-of-Table), and hybrid (TabSQLify) approaches. KG methods involve loose coupling (e.g., Chain-of-Knowledge for retrieval) and tight coupling (e.g., ToG for embeddings), with multi-modal extensions incorporating semantic segmentation for visual data processing [21].

KG-Enhanced LLMs and LLM-Augmented KGs

KG-enhanced LLMs, like RoG and GNN-RAG, improve reasoning and reduce hallucinations. LLM-augmented KGs automate entity linking and relation extraction, with frameworks like Pan et al. (2024) unifying alignment [22]. Multi-modal systems integrate semantic segmentation to enrich KGs with visual entities, enhancing reasoning in tasks like visual question answering (VQA).

Figure 5: KG-Enhanced LLMs and LLM-Augmented KGs

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Evaluation Benchmarks and Protocols

Benchmarks like WebQSP test complete KGs, while AMIE3-based datasets simulate incompleteness. The VINE dataset evaluates virtual extraction, and COCO assesses semantic segmentation for multi-modal reasoning, revealing LLMs' strengths and limitations [23].

Applications

The integration of Large Language Models (LLMs) with Knowledge Graphs (KGs) within a neuro-symbolic framework has transcended theoretical research, delivering transformative solutions across industries where structured knowledge ensures accuracy, context-awareness, and adaptability. By leveraging KGs to ground LLMs in verifiable facts, these systems enable complex reasoning, reduce errors, and support dynamic updates in response to evolving data. The incorporation of multi-modal approaches, particularly combining KGs with semantic segmentation and Retrieval-Augmented Generation (RAG), extends these capabilities to visual and textual tasks, addressing complex real-world challenges. This section details applications in healthcare and multi-modal reasoning, referencing specific implementations to illustrate their impact and demonstrating the versatility of neuro-symbolic systems in practical, scalable solutions.

Healthcare and Biomedical Research

In healthcare, LLM-KG hybrids power drug discovery and clinical decision support. The MedKGent framework constructs temporally evolving KGs from millions of PubMed abstracts, extracting drug-disease relations to support drug repurposing with high accuracy, updated daily to reflect new research findings [24]. For example, it identifies novel therapeutic applications by linking drugs to diseases through relational patterns, enabling researchers to explore treatments for rare conditions. Similarly, BioBERT integrated with KGs enables enterprise search in clinical databases, extracting entities like symptoms and diagnoses to reduce diagnostic errors, as seen in hospital systems for patient outcome prediction, where structured medical knowledge improves reliability over traditional LLMs [25].

Multi-Modal Reasoning with KGs, Semantic Segmentation, and RAG

Multi-modal applications combine KGs, semantic segmentation, and RAG to enhance reasoning in tasks like medical image analysis and autonomous navigation. In medical imaging, LLMs integrated with semantic segmentation models (e.g., U-Net) extract entities like tumors from scans, enriching KGs with visual data. These KGs are queried via RAG to support diagnostic reasoning, as seen in systems for detecting abnormalities in radiology reports, improving accuracy over unimodal LLMs by grounding diagnoses in structured knowledge [26]. In autonomous vehicles, KGs model road entities (e.g., "traffic sign" linked to "speed limit"), with semantic segmentation identifying objects in real-time camera feeds. RAG retrieves relevant KG subgraphs to inform navigation decisions, enhancing safety in dynamic environments, as demonstrated in systems for self-driving cars [27].

Figure 6: Multi-Modal Reasoning with KG

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Critical Discussion and Comparison

Neuro-symbolic integrations of LLMs and KGs offer a spectrum of approaches, each with distinct strengths and challenges that shape their applicability. Symbolic-to-LLM methods, which infuse logical rules into neural models, excel in generating structured data for tasks like mathematical reasoning or policy inference, ensuring interpretable outputs suitable for domains requiring transparency, such as legal analysis. However, these methods demand significant computational resources, limiting scalability in resource-constrained settings like edge devices or real-time applications. LLM-to-symbolic approaches, such as tool-aided frameworks like Tora, leverage external symbolic solvers to enhance factual accuracy, particularly in tasks requiring precise logical inference, but introduce latency due to iterative interactions, which can hinder performance in time-sensitive contexts like financial trading.

Hybrid table reasoning methods balance interpretability and adaptability, outperforming purely symbolic approaches in tasks like structured query answering by handling noisy or incomplete data, such as ambiguous medical terminology. However, they risk error propagation in complex datasets, particularly in specialized domains where context is critical. In incomplete knowledge scenarios, KG-augmented RAG systems show varied performance: training-based methods generalize better across domains by learning robust representations, but require extensive labeled data, making them resource-intensive, while non-trained retrieval-based approaches are lightweight but struggle with novel contexts due to reliance on KG quality. Advanced LLMs excel in reasoning tasks due to their contextual learning capabilities, but falter in domain-specific extraction when faced with noisy datasets, emphasizing the need for high-quality KGs. Bias propagation remains a critical challenge, as LLMs inherit errors or biases from training corpora or imperfect KGs, particularly in underrepresented domains like low-resource languages. Scalability issues persist, with tight-coupling methods increasing complexity, favoring larger models over efficient ones. Benchmarks often fail to isolate true reasoning, as direct retrieval masks logical deficiencies, underscoring the need for rigorous evaluation protocols.

Future Tracks

The future of neuro-symbolic AI lies in addressing current limitations and expanding LLM-KG integrations to meet complex real-world demands. Multi-modal reasoning, integrating KGs with semantic segmentation and vision-language models, is critical for tasks like visual question answering (VQA) and autonomous navigation, where structured knowledge grounds visual data to enhance accuracy and context-awareness [28]. Dynamic KG updating mechanisms are essential for real-time applications, such as financial forecasting or medical diagnostics, ensuring adaptability to evolving data without requiring full model retraining [29]. Addressing data reliability and ethical biases requires robust frameworks to mitigate error propagation and ensure fairness, particularly in sensitive domains like healthcare and legal analysis. Expanding benchmarks for incomplete knowledge scenarios and multi-agent systems like AutoKG will drive automation in KG construction and maintenance, improving scalability and accessibility [31]. Generalization under missing evidence will enhance robustness, supporting the development of AGI capable of handling ambiguous or sparse data in real-world settings.

Proposed Research Tracks in Neuro-Symbolic AI

Enhancing Conversational AI with Structured Knowledge

Conversational AI systems, like chatbots, often struggle to maintain accuracy in long dialogues due to limited memory, leading to errors or inconsistencies. This research explores how structured knowledge frameworks can serve as an external memory to improve the factual reliability of AI conversations, especially in applications like customer support.

Objective

The goal is to develop a hybrid AI system that combines language models with structured knowledge to reduce errors in extended conversations with limited memory. By grounding responses in verified information, the system aims to enhance accuracy and consistency, paving the way for more reliable conversational AI in real-world scenarios.

Approach

• Data Collection: Gather diverse conversation data from public sources to simulate real-world dialogue challenges.
• Knowledge Framework: Use advanced language models to build and maintain a structured knowledge base, extracting key information from dialogues to ensure factual grounding.
• Response Generation: Integrate the knowledge base with language models to generate accurate responses, adapting to evolving conversation contexts.
• Evaluation: Assess performance using standard metrics for factual accuracy and reasoning, comparing against existing conversational AI systems.

Expected Impact

This initiative aims to show that structured knowledge can significantly improve the reliability of conversational AI, reducing errors in dynamic dialogues. Expected outcomes include a prototype system and insights into scaling such solutions, contributing to more trustworthy AI for industries like customer service.

Multi-Modal AI for Improved Medical Diagnostics

Accurate medical diagnoses often require combining visual data, like medical images, with textual information, such as clinical reports. This research proposes a hybrid AI system that integrates visual and textual knowledge to enhance diagnostic accuracy, particularly for tasks like detecting abnormalities in radiology.

Objective

The initiative seeks to create an AI system that combines visual analysis, language processing, and structured knowledge to improve diagnostic precision in healthcare. By unifying data from images and texts, the system aims to provide context-aware, reliable diagnoses, addressing challenges like incomplete or ambiguous information.

Approach

• Data Collection: Curate publicly available medical imaging and textual data to simulate real-world diagnostic scenarios.
• Visual Analysis: Apply advanced image processing techniques to identify key features in medical images, such as abnormalities or anatomical structures.
• Text Processing: Use language models to extract relevant information from clinical texts, building a structured knowledge base.
• Knowledge Integration: Combine visual and textual data into a unified framework to support reasoning and decision-making.
• Evaluation: Measure diagnostic accuracy using industry-standard metrics, comparing against traditional AI approaches.

Expected Impact

The initiative expects to demonstrate that integrating visual and textual data enhances diagnostic accuracy, offering a scalable solution for healthcare. Outcomes include a prototype system and a report on scalability and ethical considerations, advancing AI-driven tools to support clinicians in delivering precise diagnoses.

Conclusion

The neuro-symbolic integration of Large Language Models (LLMs) with Knowledge Graphs (KGs) represents a transformative advancement in addressing the inherent limitations of LLMs, such as hallucinations and context window constraints, fostering AI systems that combine neural flexibility with symbolic precision. Empirical surveys from 2024-2025 demonstrate that these hybrids significantly enhance factual accuracy and enable robust multi-hop reasoning, as evidenced by their successful deployment in diverse domains like healthcare and multi-modal reasoning. For instance, systems like MedKGent for drug repurposing and multi-modal frameworks for medical imaging showcase the power of integrating structured knowledge with LLMs and semantic segmentation to deliver scalable, accurate solutions. Challenges in data quality, ethical deployment, and computational efficiency persist, necessitating innovation in dynamic updating and bias mitigation. The proposed research initiatives—one on dynamic KGs for hallucination mitigation in limited-context conversations, and another on multi-modal reasoning for medical diagnostics—address critical gaps, offering practical approaches to enhance factual consistency and diagnostic accuracy. By advancing multi-modal reasoning, rigorous benchmarks, and ethical frameworks, neuro-symbolic AI can drive trustworthy, adaptable systems toward AGI, delivering societal benefits through intelligent, context-aware, and ethically sound solutions.