LLM repairs Knowledge Graph (Apple MacBook)

Introduction

Today let explores groundbreaking research on using Large Language Models (LLMs) to automatically repair inconsistencies in knowledge graphs. It examines a recent study from CNRS (France) demonstrating how open-source LLMs can detect and correct semantic errors in graph structures, with a focus on medical applications. Notably, the research was conducted entirely on a consumer MacBook Pro M3 Max, making advanced AI research accessible to individual researchers and students. Video demonstration included.

Knowledge Graph Inconsistencies: Problems and Repair Strategies

What Are Knowledge Graphs?

Knowledge graphs are structured representations of information that model relationships between entities using nodes (representing entities) and edges (representing relationships). They’re widely used in domains like healthcare, finance, e-commerce, and search engines to organize and reason about complex interconnected data.

Types of Inconsistencies in Knowledge Graphs

Semantic Inconsistencies

• Contradictory facts: Same entity having conflicting properties (e.g., a person being both alive and deceased)
• Logical violations: Relationships that violate domain rules (e.g., a patient prescribed medication they’re allergic to)
• Type mismatches: Entities assigned incompatible types or categories

Structural Inconsistencies

• Missing relationships: Expected connections between entities that are absent
• Incorrect relationships: Wrong relationship types between entities
• Orphaned nodes: Entities with no meaningful connections
• Duplicate entities: Same real-world entity represented multiple times

Data Quality Issues

• Incomplete information: Missing attributes or properties
• Outdated data: Information that’s no longer current
• Inconsistent naming: Same entity referenced with different identifiers
• Format inconsistencies: Different data formats for similar information

Why Inconsistencies Are Problematic

Downstream Applications Impact

• Decision-making errors: Automated systems making incorrect recommendations
• Search and retrieval issues: Users getting incomplete or contradictory results
• Inference problems: Reasoning systems drawing wrong conclusions

Domain-Specific Risks

• Healthcare: Life-threatening medication errors, misdiagnoses
• Finance: Incorrect risk assessments, compliance violations
• Legal: Faulty case precedent matching, regulatory compliance issues

System Reliability

• Cascading errors: Single inconsistency affecting multiple downstream processes
• Reduced trust: Users losing confidence in system recommendations
• Maintenance overhead: Increased manual correction requirements

Traditional Repair Approaches

Rule-Based Systems

How it works: Domain experts manually write rigid rules that define valid and invalid patterns

• Advantages: Precise, predictable, domain-specific
• Disadvantages: Labor-intensive, brittle, doesn’t scale well
• Example: “IF patient allergic to ingredient X AND medication contains X THEN flag inconsistency”

Constraint-Based Validation

How it works: Define formal constraints that the graph must satisfy

• Graph denial constraints: Specify patterns that should never exist
• Integrity constraints: Define required relationships and properties
• Cardinality constraints: Limit number of relationships per entity

Manual Human Review

How it works: Domain experts manually review and correct inconsistencies

• Advantages: High accuracy, contextual understanding
• Disadvantages: Extremely time-consuming, doesn’t scale, expensive

Statistical and Machine Learning Methods

How it works: Use pattern recognition to identify anomalies

• Outlier detection: Identify entities or relationships that deviate from normal patterns
• Clustering analysis: Group similar entities to find misclassifications
• Embedding-based approaches: Use graph embeddings to detect inconsistencies

Modern LLM-Based Repair Approaches

Detection Phase

1. Pattern Identification: Use graph queries to find potentially inconsistent subgraphs
2. Localization: Isolate specific problematic areas within the larger graph
3. Context Extraction: Gather relevant surrounding information

Representation Conversion

Transform graph data into LLM-readable format:

• Raw format: Direct node/edge listings with technical identifiers
• Template-based: Human-readable sentences with structured placeholders
• AI-summarized: Use one LLM to create natural language summaries

LLM Processing

1. Inconsistency Analysis: LLM examines the problematic subgraph
2. Repair Generation: LLM suggests specific modification operations
3. Structured Output: Return repairs in standardized format (e.g., “DELETE edge X”, “ADD relationship Y”)

Validation and Application

• Semantic validation: Check if proposed repairs make domain sense
• Consistency checking: Ensure repairs don’t introduce new inconsistencies
• Human review: Critical repairs reviewed by domain experts

Challenges with LLM-Based Approaches

Fundamental Limitations

• Structural vs. Semantic Reasoning: LLMs excel at following patterns but struggle with factual validation
• Domain Knowledge Gaps: May lack deep understanding of specialized domains
• Hallucination Risk: May generate plausible-sounding but incorrect repairs

Quality and Accuracy Issues

• Format adherence: High success rate (90%+) in following output formatting
• Repair validity: Moderate success in generating structurally valid repairs
• Semantic correctness: Low success rate (often <40%) in meaningful, accurate repairs

Scalability Concerns

• Context limitations: LLMs have finite context windows for large graphs
• Computational cost: Processing large graphs requires significant resources
• Fine-tuning requirements: Domain-specific performance may require extensive training data

Hybrid and Advanced Approaches

Neurosymbolic Methods

Combine LLMs with symbolic reasoning systems:

• LLM for pattern recognition: Identify potential inconsistencies
• Symbolic system for validation: Apply formal logic and domain rules
• Iterative refinement: Multiple passes to improve accuracy

Multi-Agent Systems

• Specialist agents: Different LLMs trained for specific types of inconsistencies
• Validation agents: Dedicated systems for checking proposed repairs
• Coordination mechanisms: Orchestrate multiple agents for complex repairs

Human-in-the-Loop Systems

• Confidence scoring: Flag uncertain repairs for human review
• Interactive refinement: Allow humans to guide and correct LLM reasoning
• Explanation systems: Provide detailed justifications for proposed repairs

Best Practices for Implementation

Start Small and Iterate

• Begin with simple, well-defined inconsistency patterns
• Test thoroughly on controlled datasets
• Gradually expand to more complex scenarios

Implement Strong Validation

• Multiple validation layers before applying repairs
• Rollback mechanisms for incorrect changes
• Comprehensive logging and audit trails

Domain-Specific Customization

• Fine-tune models on domain-specific data
• Incorporate domain expertise in validation rules
• Regular retraining as knowledge evolves

Monitoring and Maintenance

• Continuous monitoring of repair quality
• Regular evaluation against ground truth data
• Feedback loops to improve system performance

Future Directions

The field is moving toward more sophisticated approaches that combine the pattern recognition capabilities of LLMs with the precision of symbolic reasoning systems. Success will likely require domain-specific fine-tuning, robust validation frameworks, and careful human oversight, especially in critical applications like healthcare and finance.

The ultimate goal is developing systems that can maintain large-scale knowledge graphs automatically while ensuring the semantic correctness and factual accuracy that current LLM-only approaches struggle to achieve.

Video about how LLM Repair inconsistencies KG:

Is Using LLMs to Repair Knowledge Graph Inconsistencies a Good Solution?

Short Answer: Partially, but with significant caveats

LLMs show promise for certain aspects of knowledge graph repair, but current research indicates they’re not yet ready as standalone solutions for critical applications. They work best as assistive tools within hybrid systems rather than autonomous repair agents.

Current Capabilities vs. Limitations

What LLMs Do Well ✅

• Pattern Recognition: Excellent at identifying structural inconsistencies and following repair patterns
• Format Adherence: >90% success rate in producing properly structured outputs
• Scalability: Can process large volumes of data quickly
• Accessibility: Can run on consumer hardware, democratizing access to graph repair tools
• Flexibility: Can handle diverse domains without extensive rule programming

Critical Limitations ⚠️

• Low Semantic Accuracy: Often <40% accuracy for meaningful repairs in specialized domains
• Structural vs. Factual Reasoning Gap: Excel at logical patterns but struggle with domain knowledge validation
• Hallucination Risk: May generate plausible-sounding but factually incorrect repairs
• Overgeneration: Tendency to suggest unnecessary changes that can introduce new problems
• Context Limitations: Difficulty processing extremely large graph contexts

Domain-Specific Considerations

High-Risk Domains 🚫

Not recommended as primary solution:

• Healthcare: 40% accuracy insufficient for medication/treatment decisions
• Finance: Regulatory compliance requires near-perfect accuracy
• Legal: Case law and precedent matching demands factual precision
• Safety-Critical Systems: Any domain where errors could cause harm

Medium-Risk Domains ⚡

Useful with human oversight:

• E-commerce: Product recommendations and categorization
• Content Management: Organizing and linking digital content
• Academic Research: Literature and citation networks
• General Knowledge Bases: Wikipedia-style information systems

Lower-Risk Domains ✅

More suitable for LLM-based repair:

• Social Networks: Connection suggestions and profile completion
• Entertainment: Content recommendations and metadata
• General Web Search: Non-critical information retrieval
• Experimental Research: Where errors are acceptable for learning

When LLMs Can Be Effective

Appropriate Use Cases

1. Initial Screening: Flagging potential inconsistencies for human review
2. Batch Processing: Handling large volumes of low-risk inconsistencies
3. Pattern Learning: Identifying common inconsistency types to inform rule creation
4. Prototype Development: Rapid testing of repair strategies before formal implementation
5. Data Exploration: Understanding the nature and extent of inconsistencies

Effective Implementation Strategies

• Confidence Thresholds: Only apply high-confidence repairs automatically
• Human-in-the-Loop: Route uncertain cases to domain experts
• Validation Layers: Multiple checking mechanisms before applying changes
• Rollback Capabilities: Easy reversal of incorrect modifications
• Audit Trails: Comprehensive logging for accountability

Alternative and Hybrid Approaches

Better Near-Term Solutions

Neurosymbolic Systems

• LLMs for pattern detection + symbolic reasoning for validation
• Combines LLM flexibility with logical precision
• Better semantic grounding through formal rules

Multi-Agent Architectures

• Specialist agents for different inconsistency types
• Validation agents for cross-checking repairs
• Coordination systems for complex scenarios

Enhanced Rule-Based Systems

• LLM-assisted rule generation and refinement
• Automated rule discovery from graph patterns
• Hybrid rule-ML validation systems

Human-AI Collaboration

• LLMs as intelligent assistants to human experts
• Automated flagging with expert decision-making
• Interactive repair with AI suggestions

Promising Research Directions

• Domain-Specific Fine-Tuning: Training LLMs on specialized knowledge
• Retrieval-Augmented Generation: Grounding LLM reasoning in factual databases
• Tool-Using LLMs: Enabling LLMs to query authoritative sources
• Explanation Systems: Making LLM reasoning more transparent and verifiable

Practical Recommendations

For Organizations Considering LLM-Based Repair

Start Small

• Begin with non-critical domains and simple inconsistency types
• Pilot with extensive human oversight
• Measure accuracy rigorously before scaling

Implement Safeguards

• Multiple validation layers
• Confidence scoring and thresholds
• Easy rollback mechanisms
• Comprehensive audit trails

Hybrid Approach

• Use LLMs for detection and initial analysis
• Apply symbolic reasoning for validation
• Maintain human oversight for critical decisions
• Combine with traditional rule-based systems

Continuous Evaluation

• Regular accuracy assessments
• Domain expert feedback loops
• Performance monitoring over time
• Adaptation as LLM capabilities improve

Future Outlook

Short-Term (1-2 years)

• LLMs will improve but likely remain unsuitable as standalone solutions for critical domains
• Hybrid systems will become more sophisticated and reliable
• Better fine-tuning approaches will emerge for specialized domains

Medium-Term (3-5 years)

• Neurosymbolic approaches may achieve acceptable accuracy for more domains
• Tool-using LLMs with access to authoritative databases could improve factual grounding
• Domain-specific models may reach clinical-grade accuracy for certain applications

Long-Term (5+ years)

• Advanced reasoning capabilities may bridge the semantic gap
• Integration with knowledge bases and validation systems may enable autonomous operation
• New architectures designed specifically for knowledge graph reasoning may emerge

LLMs are promising but premature as standalone solutions for knowledge graph repair, especially in critical domains. They’re most valuable as:

1. Detection tools for identifying potential inconsistencies
2. Assistant systems supporting human experts
3. Research instruments for understanding inconsistency patterns
4. Components in larger hybrid systems

For now, the safest and most effective approach combines LLM capabilities with traditional validation methods, human oversight, and domain-specific safeguards. Organizations should view LLMs as powerful tools to augment rather than replace existing knowledge graph maintenance processes.

Conclusion and Key Takeaways

Future Implications and Challenges

Neurosymbolic Approaches The research suggests that future solutions may require combining LLMs with symbolic reasoning systems to bridge the gap between structural pattern matching and factual knowledge validation.

Domain-Specific Fine-tuning Challenges Scaling this approach would require massive datasets of graph inconsistencies, violations, and correct repairs for each possible configuration – a monumental undertaking for complex medical knowledge graphs.

Enhanced Output Requirements The presenter advocates for more sophisticated output formats including structured explanations, evidence sources, API connections, reasoning traces, and confidence scores to make LLM repairs trustworthy for critical applications.

Major Takeaways:

• LLMs excel at structural pattern recognition but lack factual reasoning capabilities
• Template-based data representation works best for LLM comprehension
• Current accuracy levels (≤40%) are insufficient for medical applications
• Consumer hardware can enable meaningful AI research
• Future solutions likely require hybrid neurosymbolic approaches
• Significant fine-tuning and validation frameworks are needed before real-world deployment

Bottom Line: LLMs show promise as “graph doctors” but currently function more like medical students following procedures without deep understanding – useful for learning exercises but not yet trusted for patient care.

References

References in this Video

• Hardware: MacBook Pro M3 Max with 36GB unified memory
• Models Tested: Llama 3.2, Mistral, DeepSeek R1 (distilled), Q1 2.5, Gamma 2, among others

External References

Core Knowledge Graph Repair Research

Recent Studies on LLM-Based KG Repair

1. Dessì, D., et al. (2025). “Knowledge graph validation by integrating LLMs and human-in-the-loop.” ScienceDirect. Demonstrates that LLMs demonstrate weak performance as standalone knowledge graph validators but reach human-level quality when combined with other automated validation methods.
2. Feng, Y., et al. (2025). “Knowledge graph–based thought: a knowledge graph–enhanced LLM framework for pan-cancer question answering.” GigaScience, Volume 14. Shows improved integration approaches for combining LLMs with knowledge graphs to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning.

Traditional KG Inconsistency Detection & Repair

1. Melo, A., & Paulheim, H. (2024). “Detecting and Fixing Inconsistency of Large Knowledge Graphs.” ResearchGate. Comprehensive study on deducing certain fixes to graphs based on data quality rules and ground truth, proposing algorithms to compute repairs using Answer Set Programming (ASP).
2. Ahmeti, A., Nenov, Y., & David, R. (2024). “Repairing Inconsistencies in Data Processing for Enterprise Knowledge Graphs.” Oxford Semantic Technologies. Practical approach showing how to validate and repair inconsistencies using an automated approach that can be integrated into the ETL process.

LLM Limitations and Reasoning Challenges

Fundamental LLM Limitations in Reasoning

1. Nezhurina, M., et al. (2024). “Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models.” LAION. Reveals that even state-of-the-art Large Language Models (LLMs) as of June 2024 failed to complete simple logical tasks.
2. Raschka, S. (2025). “LLM Research Papers: The 2025 List (January to June).” Sebastian Raschka’s Magazine. Comprehensive review of training strategies specifically designed to improve reasoning abilities in LLMs, with much of the recent progress centered around reinforcement learning with verifiable rewards.

Structured Data and Graph Reasoning

1. Wang, Q., et al. (2024). “Correcting inconsistencies in knowledge graphs with correlated knowledge.” ScienceDirect. Framework for detecting and correcting inconsistent triples in KGs, identifying entity-related inconsistency, relation-related inconsistency and type-related inconsistency.
2. Horridge, M., et al. (2020). “Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs.” The Web Conference 2020. Novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns.

Neurosymbolic AI and Hybrid Approaches

Neurosymbolic AI Research

1. Anonymous Authors (2025). “Neuro-Symbolic AI in 2024: A Systematic Review.” arXiv. Systematic review showing that the goal of Neuro-Symbolic research is to create a superior hybrid AI model possessing reasoning capabilities, with 167 papers analyzed focusing on learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%).
2. IBM Research Team (2024). “Neuro-symbolic AI.” IBM Research. Position that Neuro-symbolic AI is a pathway to achieve artificial general intelligence by augmenting and combining the strengths of statistical AI with the capabilities of human-like symbolic knowledge and reasoning.
3. Sheth, A., et al. (2023). “Neurosymbolic AI — Why, What, and How.” Referenced in Towards AI. Defines Neurosymbolic AI as AI systems that combine both neural network-based methods and symbolic AI methods, leveraging the strengths of both while addressing their individual limitations.

Applications and Integration

1. den Hamer, P. (2024). “Neurosymbolic AI emerges as a potential way to fix AI’s reliability problem.” Fortune. Analysis suggesting that neurosymbolic AI seems to be one of the necessary steps to achieve AGI because we need better reasoning and more reliable intelligence than we have today.
2. Kant, M., et al. (2024). “Equitable Access to Justice: Logical LLMs Show Promise.” NeurIPS 2024 Workshop on System-2 Reasoning. Case study showing a massive leap in capability from OpenAI’s GPT-4o to OpenAI o1-preview on legal reasoning tasks, opening directions in neuro-symbolic AI applications.

Knowledge Graph Construction and Validation

LLM-Based KG Construction

1. Hunger, M. (2025). “LLM Knowledge Graph Builder — First Release of 2025.” Neo4j Developer Blog. Updates on community summaries, parallel retrievers, and expanded model support for building retrieval-augmented generation experiences using knowledge graphs.
2. Bai, M., et al. (2025). “Construction of a knowledge graph for framework material enabled by large language models and its application.” npj Computational Materials. Large-scale study constructing a comprehensive knowledge graph from over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships using natural language processing capabilities of LLMs.

Workshop and Community Resources

1. TEXT2KG Workshop Organizers (2025). “LLM-TEXT2KG 2025: 4th International Workshop on LLM-Integrated Knowledge Graph Generation from Text.” ESWC 2025. Workshop exploring the novel intersection of LLMs and KG generation, focusing on innovative approaches, best practices, and challenges in knowledge extraction and context-aware entity disambiguation.
2. ZJU KG Research Team (2025). “KG-LLM-Papers: Papers integrating knowledge graphs and large language models.” GitHub Repository. Comprehensive collection of papers integrating knowledge graphs and large language models, with recent focus on ontology-driven self-training and unified structured data question answering.

Quality Assessment and Enterprise Applications

Data Quality and Validation

1. Li, Y., et al. (2024). “From data to insights: the application and challenges of knowledge graphs in intelligent audit.” Journal of Cloud Computing. Analysis of knowledge graph technology applications in intelligent auditing, urban transportation planning, legal research, and financial analysis, highlighting challenges in data integration and analysis.
2. Heyvaert, P., et al. (2019). “Rule-driven inconsistency resolution for knowledge graph generation rules.” Semantic Web Journal. Framework using rules to detect inconsistencies within mappings and resulting datasets, addressing quality issues within mapping artefacts to improve resulting dataset quality.

Systematic Reviews and Surveys

Comprehensive Literature Reviews

1. Raschka, S. (2024). “LLM Research Papers: The 2024 List.” Sebastian Raschka’s Magazine. Curated collection of fascinating LLM-related research papers from 2024, including comprehensive studies of knowledge editing for LLMs and parameter-efficient fine-tuning approaches.
2. Ganesh, V., et al. (2024). “NeuroSymbolic LLM for mathematical reasoning and software engineering.” IJCAI 2024. Research proposing to integrate logical and symbolic feedback during the training process, enabling significantly smaller language models to achieve far better reasoning capabilities than current LLMs.