EduSmart is an advanced personalized learning platform that tailors educational content to individual students by integrating three core technologies:
- Knowledge Graphs: For structuring educational content and tracking student knowledge.
- Large Language Models (LLMs): For generating customized explanations and engaging with students in natural language.
- AI Agents: For orchestrating the learning experience, adapting to student needs, and providing real-time assistance.
Task 1: Build GQL Schema for Course Content Knowledge Graph ( https://github.com/panaversity/learn-applied-generative-ai-fundamentals/tree/main/22_knowledge_graphs_genai_agents/02_understanding_gql )
Task 2: Build GQL Schema for Course Content Questions/Interaction Knowledge Graph
Task 3: Build GQL Schema for Student Tacking and Course Personalization Knowledge Graph
Task 4: Build AI Agent (LangGraph) that Generates Learing Content and pushes metadata into a Knowledge Graph (Neo4j) and markdown including metadata (directories and https://www.npmjs.com/package/front-matter) in GitHub.
Task 5: Build AI Agent that Genenerates Questions/Interactions
Task 6: Build Chat Bot that interacts with Students, and interacts with them to track their performance and fill the learning gaps.
Task 7: Build Github Action Scripts to syncronize each push with the Knowledge Graph (Neo4j).
Task 8: Build Github Action Script to build and update Next.js Website.
Task 9: Build Github Action Script to build and update PDF Book of Learning Material.
Task 10: Build a AI Agent that generates teaching videos from content in github repo and Knowledge Graphs (may be in next iteration)
Introduction
Your proposed workflow for developing a personalized e-learning advanced AI system integrates several powerful technologies:
- Large Language Models (LLMs): Using OpenAI's GPT models to generate educational content.
- Knowledge Graphs: Utilizing Neo4j to store and manage metadata about the content.
- AI Agents: Implementing AI agents (possibly referring to LangChain) for advanced interactions.
- Version Control and Collaboration: Storing content in markdown format on GitHub to enable collaboration and version control.
- Automation: Employing GitHub Actions to synchronize the content and metadata.
- Content Delivery: Generating PDFs or HTML for students using the markdown content and metadata.
This workflow aims to combine automated content generation, human expertise, structured metadata, and automated processes to deliver personalized learning experiences.
Detailed Breakdown of Your Proposed Workflow
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Content Generation with LLMs (OpenAI)
- Process: Use LLMs to generate educational content in markdown format.
- Output: Rich textual content covering various educational topics.
- Considerations:
- Prompt Engineering: Craft prompts to guide the LLM in producing accurate and pedagogically sound content.
- Quality Assurance: Implement validation steps to check for correctness, relevance, and appropriateness.
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Storing Metadata in Knowledge Graph (Neo4j)
- Process: Extract and store metadata about the generated content in Neo4j.
- Metadata Includes:
- Topics and Subtopics: Hierarchical structure of the content.
- Learning Objectives: Goals associated with each piece of content.
- Prerequisites: Dependencies between different content pieces.
- Difficulty Levels: Classification of content based on complexity.
- Benefits:
- Personalization: Enables adaptive learning paths tailored to individual students.
- Advanced Querying: Facilitates complex queries to retrieve and organize content.
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Storing Content in GitHub (Markdown Format)
- Process: Push the generated markdown content to a GitHub repository. Including metadata in Markdown files, YAML is the more widely adopted and recommended format. This practice is commonly known as "YAML front matter," where metadata is enclosed. YAML front matter is typically placed at the very beginning of a Markdown file, enclosed between two lines containing ---. Use PyYAML or Python-Markdown with the meta extension or https://www.npmjs.com/package/front-matter
- Advantages:
- Version Control: Tracks changes and maintains a history of edits.
- Collaboration: Allows multiple educators to contribute and improve content.
- Considerations:
- Access Management: Control who can view and edit the repository.
- Branching Strategy: Use branches for development and production to manage updates.
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Human Enhancement and Editing by Teachers
- Process: Educators review, edit, and enhance the markdown content.
- Activities:
- Content Improvement: Correct errors, clarify explanations, and add examples.
- Alignment with Curriculum: Ensure content meets educational standards and learning objectives.
- Localization: Adapt content for different languages and cultural contexts.
- Workflow:
- Pull Requests: Teachers submit changes through pull requests for review.
- Code Reviews: Peers or content leads review and approve changes.
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Synchronization with Knowledge Graph via GitHub Actions
- Process: Use GitHub Actions to automate the synchronization between the markdown content and the knowledge graph.
- Steps:
- Trigger: Actions are triggered by events like pushes or merges.
- Script Execution: Scripts parse the markdown files, extract updated metadata, and update Neo4j accordingly.
- Considerations:
- Error Handling: Implement logging and notifications for synchronization failures.
- Security: Protect credentials and ensure secure connections to Neo4j.
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Content Delivery to Students (PDF/HTML)
- Process: Generate consumable content for students using the markdown files and metadata. In beginning we can just use Github website for this.
- Methods:
- Static Site Generators: Use tools like Next.js, Jekyll or Hugo to convert markdown to HTML.
- PDF Generation: Utilize markdown-to-PDF converters for offline access.
- Personalization:
- Dynamic Assembly: Generate personalized content packages based on the student's profile and progress.
- Adaptive Paths: Use the knowledge graph to recommend the next topics.
Detailed Recommendations for Your Workflow
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Content Generation with LLMs
- Prompt Design:
- Be specific in prompts to guide the LLM towards generating structured and relevant content.
- Include examples or templates within prompts to standardize output formats.
- Content Validation:
- Implement automated checks for grammar, plagiarism, and adherence to learning objectives.
- Use AI tools to assess the readability and difficulty level.
- Prompt Design:
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Metadata Management in Neo4j
- Schema Design:
- Define clear entity types (e.g., Lesson, Topic, Concept, Prerequisite) and relationships.
- Use GQL Schemas to standardize.
- Data Consistency:
- Ensure synchronization scripts handle updates, deletions, and conflicts gracefully.
- Implement versioning for metadata to track changes over time.
- Schema Design:
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Content Storage and Collaboration in GitHub
- Repository Structure:
- Organize content logically (e.g., by chapter, topic, subtopic).
- Use consistent naming conventions for files and directories.
- Access Control:
- Use GitHub Teams and permissions to manage collaborator access.
- Protect the main branch and require reviews for merges.
- Continuous Integration (CI):
- Set up CI pipelines to automatically build and test content upon changes.
- Include checks for markdown linting and link validation.
- Repository Structure:
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Teacher Involvement
- Training and Support:
- Provide training sessions for teachers on using Git and GitHub.
- Create documentation and guidelines for content contribution.
- Contribution Guidelines:
- Define standards for content quality, style, and formatting.
- Encourage the use of descriptive commit messages and pull request descriptions.
- Recognition and Incentives:
- Acknowledge contributions publicly to motivate ongoing participation.
- Training and Support:
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GitHub Actions and Automation
- Workflow Automation:
- Automate tasks like content validation, metadata extraction, and deployment.
- Use actions to trigger builds of PDF/HTML content after successful merges.
- Security Measures:
- Store sensitive information like database credentials securely using GitHub Secrets.
- Limit the scope of tokens and credentials to necessary permissions.
- Monitoring and Alerts:
- Set up alerts for failed workflows or synchronization issues.
- Regularly review logs to identify and address recurring problems.
- Workflow Automation:
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Content Delivery and Personalization
- Dynamic Content Generation:
- Develop a middleware layer or use existing tools to assemble content dynamically based on student profiles.
- Implement caching mechanisms to improve performance.
- Responsive Design:
- Ensure HTML content is mobile-friendly and accessible on various devices.
- Accessibility Compliance:
- Follow WCAG guidelines to make content accessible to learners with disabilities.
- Provide alternative text for images and ensure proper semantic markup.
- Dynamic Content Generation:
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Integration of AI Agents (LangGraph)
- Functionality:
- Use AI agents to provide interactive tutoring, answer student queries, or generate additional explanations.
- Integrate with the knowledge graph to enhance the contextual relevance of AI responses.
- Ethical Considerations:
- Ensure AI interactions are monitored to prevent misinformation.
- Provide clear disclaimers about the use of AI in communications.
- Functionality:
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Student Data Management
- Learning Record Store (LRS) and xAPI:
- Implement an LRS to collect and analyze student interaction data.
- Use insights to refine content and personalize learning experiences.
- Data Privacy:
- Comply with data protection regulations like GDPR or COPPA.
- Obtain necessary consents and provide transparency about data usage.
- Learning Record Store (LRS) and xAPI:
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Feedback Loop
- Continuous Improvement:
- Collect feedback from students and educators to improve content and the platform.
- Implement mechanisms for users to report errors or suggest enhancements.
- Analytics and Reporting:
- Use data from the LRS and knowledge graph to generate reports on content effectiveness.
- Adjust learning paths based on performance metrics.
- Continuous Improvement:
Potential Challenges and Mitigation Strategies
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Content Quality Control
- Challenge: Ensuring the accuracy and educational value of AI-generated content.
- Solution: Establish a robust review process involving educators before content is published.
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Technical Complexity
- Challenge: Integrating multiple technologies and maintaining synchronization between them.
- Solution: Modularize components and define clear interfaces and protocols for interaction.
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Scaling the System
- Challenge: Handling increased load as the number of users grows.
- Solution: Use scalable cloud services for hosting Neo4j and content delivery infrastructure.
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User Adoption
- Challenge: Encouraging educators to adopt new tools and workflows.
- Solution: Provide user-friendly interfaces, comprehensive training, and ongoing support.
Additional Recommendations
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Documentation and Knowledge Sharing
- Maintain comprehensive documentation for developers, educators, and users.
- Use wikis or documentation generators to keep information up-to-date.
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Security Best Practices
- Regularly update dependencies and monitor for vulnerabilities.
- Conduct security audits and penetration testing to identify and address weaknesses.
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Disaster Recovery and Backup
- Implement regular backups of the knowledge graph and content repositories.
- Develop a disaster recovery plan to restore services in case of failures.
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Legal and Licensing Considerations
- Clarify content licensing, especially if combining AI-generated content with human edits.
- Ensure compliance with intellectual property laws.
Conclusion
Your proposed workflow effectively leverages the strengths of LLMs, knowledge graphs, collaborative platforms, and automation to create a dynamic and personalized e-learning system. By combining automated content generation with human expertise, you can maintain high-quality educational materials while benefiting from the efficiency of AI.
Implementing the recommendations provided can help address potential challenges and enhance the overall effectiveness of your system. Focus on seamless integration, robust quality assurance processes, and user engagement to ensure the success of your platform.
OpenAI o1 EduSmart: A Personalized Learning System Integrating Knowledge Graphs, LLMs, and AI Agents
Note: EduSmart is a hypothetical example designed to illustrate how a personalized learning system can integrate knowledge graphs, large language models (LLMs), and AI agents to enhance educational experiences.
EduSmart is an advanced personalized learning platform that tailors educational content to individual students by integrating three core technologies:
- Knowledge Graphs: For structuring educational content and tracking student knowledge.
- Large Language Models (LLMs): For generating customized explanations and engaging with students in natural language.
- AI Agents: For orchestrating the learning experience, adapting to student needs, and providing real-time assistance.
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Curriculum Mapping: EduSmart uses knowledge graphs to map out the entire curriculum across subjects like mathematics, science, and languages. Each concept is a node, and relationships between concepts are edges, creating a network that represents how ideas interconnect.
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Student Knowledge Profiles: Every student has a personalized knowledge graph reflecting their understanding. As they learn, their graph updates to show mastery over certain topics and identify areas needing improvement.
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Personalized Content Generation: LLMs like GPT-4 generate explanations, examples, and analogies tailored to the student's learning style and current understanding.
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Interactive Q&A: Students can ask questions in natural language, and the LLM provides clear, context-aware answers, enhancing comprehension.
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Learning Companion: An AI agent acts as a virtual tutor, guiding students through the curriculum, suggesting resources, and providing encouragement.
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Adaptive Learning Pathways: The AI agent analyzes performance data to adjust the learning pathway in real-time, ensuring optimal challenge and engagement levels.
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Diagnostic Tests: Upon joining, students take assessments to gauge their current knowledge.
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Knowledge Graph Initialization: Results populate the student's knowledge graph, highlighting known concepts and gaps.
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Customized Pathways: The AI agent designs a learning path based on the knowledge graph, focusing on prerequisites and building towards advanced topics.
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Goal Setting: Students set learning goals (e.g., mastering algebraic equations), which the system incorporates into the plan.
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Dynamic Content Delivery: Lessons adapt in complexity and style. For visual learners, more diagrams are provided; for textual learners, detailed explanations are emphasized.
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Real-Time Assistance: If a student struggles, the AI agent intervenes with hints or alternative explanations generated by the LLM.
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Micro-Assessments: Short quizzes after each concept check understanding.
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Progress Updates: The knowledge graph updates to reflect new competencies, and the AI agent adjusts the learning plan accordingly.
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Gamification: Badges and points are awarded for milestones, encouraging continued effort.
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Peer Interaction: Discussion forums moderated by AI facilitate peer learning and collaboration.
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Concept Linking: Concepts like "photosynthesis" link to "light energy," "chlorophyll," and "glucose production," helping students see the bigger picture.
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Prerequisite Mapping: The system ensures foundational concepts are mastered before introducing more complex ideas.
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Natural Language Processing: The LLM understands student queries, even with grammatical errors or colloquial language.
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Content Adaptation: Generates examples relevant to the student's interests (e.g., using football analogies for physics concepts if the student is a sports enthusiast).
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Behavioral Analysis: Monitors patterns like time spent on tasks or repeated mistakes to identify when a student is disengaged or frustrated.
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Intervention Strategies: Suggests breaks, switches topics, or introduces interactive elements like videos or simulations.
Student Profile: Alex, a 15-year-old student struggling with quadratic equations.
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Initial Test: Reveals gaps in understanding factoring and the quadratic formula.
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Knowledge Graph Update: Nodes related to these concepts are flagged for attention.
- Learning Modules Assigned: Interactive lessons on factoring techniques and derivation of the quadratic formula.
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Lesson Delivery: The LLM presents a step-by-step explanation, using real-world problems (e.g., projectile motion in sports).
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Interactive Questions: Alex asks, "Why do we set the equation to zero?" The LLM provides a tailored explanation.
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Quiz Performance: Alex struggles with complex problems.
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AI Agent Adjustment: Introduces additional practice on specific problem types, perhaps with gamified elements to boost engagement.
- Knowledge Graph Update: As Alex improves, the graph reflects mastery, and new, more challenging topics are introduced.
- Customized Learning Paths: Ensures students focus on what they need to learn, not a one-size-fits-all curriculum.
- Interactive Content: Keeps students interested with varied content formats and interactive elements.
- Adaptive Learning: Accelerates through known material, spends more time on challenging topics.
- 24/7 Availability: Students can learn at their own pace, anytime and anywhere.
- Sensitive Information: Safeguarding student data with encryption and compliance with regulations like GDPR.
- Fairness in AI: Ensuring LLM outputs are free from biases and culturally sensitive.
- Resource Intensive: Requires robust infrastructure for real-time processing and AI computations.
- Educator Involvement: Teachers monitor progress and can intervene, ensuring the AI's suggestions align with educational best practices.
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Multimodal Learning: Integrating AR/VR for immersive learning experiences.
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Emotional AI: Detecting student emotions to further tailor interactions.
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Global Collaboration: Connecting students worldwide for cross-cultural learning opportunities.
EduSmart demonstrates the powerful synergy of knowledge graphs, LLMs, and AI agents in creating a personalized learning ecosystem. By harnessing these technologies, EduSmart not only enhances individual learning outcomes but also represents a significant step forward in the evolution of educational platforms.
Key Takeaways:
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Integrated Technologies: The combination leads to a more responsive and effective learning environment.
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Student-Centric Approach: Focuses on individual needs, promoting better engagement and retention.
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Scalable Solution: Capable of adapting to numerous subjects and educational levels, making quality education accessible to a broader audience.
This example illustrates how such a system could function and the potential benefits and challenges involved. It serves as a conceptual model for educators, technologists, and policymakers interested in the future of personalized education.
Let's consider a personalized learning system called EduSmart that integrates knowledge graphs (KGs), large language models (LLMs), and AI agents to provide a tailored educational experience.
EduSmart uses KGs to structure and organize educational content. The KG includes nodes representing concepts, topics, and skills, and edges representing relationships between them. For example, in a mathematics KG, nodes might include "Algebra," "Quadratic Equations," and "Factoring," with edges showing how these topics are related.
LLMs like GPT-4 are used to understand and generate natural language. In EduSmart, LLMs help in:
- Content Generation: Creating personalized explanations, summaries, and examples based on the student's current understanding and progress.
- Question Answering: Providing instant answers to student queries, leveraging the structured information from the KG to ensure accuracy.
AI agents in EduSmart act as virtual tutors, guiding students through their learning journey. They use both the KG and LLM to:
- Personalized Learning Paths: Analyze the student's performance and preferences to recommend the next topics or activities. For instance, if a student struggles with "Quadratic Equations," the agent might suggest revisiting "Factoring" before moving forward.
- Interactive Tutoring: Engage in dialogues with students, asking questions, providing hints, and offering feedback. The agent can adapt its teaching style based on the student's responses and engagement levels.
Imagine a high school student, Alex, using EduSmart to study mathematics.
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Initial Assessment: Alex starts with a diagnostic test. The AI agent analyzes the results and identifies that Alex is proficient in basic algebra but needs help with quadratic equations.
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Personalized Learning Path: The AI agent creates a learning path for Alex, starting with a review of factoring techniques, followed by progressively challenging problems on quadratic equations.
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Interactive Sessions: During study sessions, Alex interacts with the AI agent. If Alex asks, "How do I factor this equation?", the LLM generates a step-by-step explanation, referencing the KG to ensure the explanation is accurate and contextually relevant.
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Real-Time Feedback: As Alex works through problems, the AI agent provides real-time feedback. If Alex makes a mistake, the agent offers hints and additional practice problems.
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Progress Tracking and Adaptation: The system continuously tracks Alex's progress. If Alex masters a topic quickly, the AI agent adjusts the learning path to introduce more advanced topics sooner. Conversely, if Alex struggles, the agent provides additional resources and support.
- Personalization: Tailors the learning experience to individual needs, ensuring that each student receives the right level of challenge and support.
- Engagement: Interactive and adaptive tutoring keeps students engaged and motivated.
- Efficiency: Helps students learn more effectively by focusing on areas that need improvement and reinforcing strengths.
By combining the structured knowledge of KGs, the language capabilities of LLMs, and the adaptive guidance of AI agents, EduSmart offers a comprehensive and personalized learning experience.
Source: Conversation with Copilot, 10/8/2024 (1) FOKE: A Personalized and Explainable Education Framework ... - Springer. https://link.springer.com/chapter/10.1007/978-981-97-5803-6_24. (2) AI and personalized learning: bridging the gap with modern educational .... https://arxiv.org/html/2404.02798v1. (3) The Role of Enterprise Knowledge Graphs in LLMs. https://www.analyticsvidhya.com/blog/2023/11/the-role-of-enterprise-knowledge-graphs-in-llms/. (4) [2407.04363] AriGraph: Learning Knowledge Graph World Models with .... https://arxiv.org/abs/2407.04363. (5) Leveraging the Power of Knowledge Graphs: Enhancing Large Language .... https://www.wisecube.ai/blog/leveraging-the-power-of-knowledge-graphs-enhancing-large-language-models-with-structured-knowledge/.