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• Compliance with Data Standards: Ensure the knowledge graph adheres to relevant data modeling standards. Where applicable, utilize standardized vocabularies and ontologies.
• Interoperability: Design the system for various graph databases and query languages. Support integration with external data sources and systems.

Answer 2:

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Knowledge Graph-Guided Retrieval Mechanisms

Description: KG-Guided Retrieval Mechanisms involve using, for example, knowledge graphs or vector databases to enhance the retrieval process in RAG systems. Knowledge graphs provide a structured representation of knowledge, enabling more precise and contextually aware information retrieval. This approach can directly query knowledge graphs or use them to augment queries to other data sources, improving the relevance and accuracy of the retrieved information.

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• Compliance with Data Standards: Ensure the knowledge graph adheres to relevant data modeling standards. Where applicable, utilize standardized vocabularies and ontologies.
• Interoperability: Design the system for various graph databases and query languages. Support integration with external data sources and systems.

Answer 3: Hybrid

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RAG Combining KGs and Dense Vectors

Draft from Daniel Burkhardt: 

Short definition/description of this topicDescription: Hybrid Retrieval combines the strengths of knowledge graphs and dense vector representations to improve information retrieval. This approach leverages the structured, relational data from knowledge graphs and the semantic similarity captured by dense vectors, resulting in enhanced retrieval capabilities. Hybrid retrieval systems can improve semantic understanding and contextual insights while addressing scalability and integration complexity challenges. 

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• Knowledge Graph Construction and Maintenance: Creating and updating high-quality knowledge graphs for specific domains can be challenging and resource-intensive.
• Scalability and Efficiency: Retrieving information from large and complex knowledge graphs while maintaining acceptable response times remains challenging.
• Evaluation Standardization: The lack of widely accepted benchmarks and evaluation metrics hinders progress and comparability across Graph RAG approaches. The quality of KG is crucial.
• Human Element, we need knowledge engineers and domain specialists. 

References

• [1] Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang: Graph Retrieval-Augmented Generation: A Survey. CoRR abs/2408.08921 (2024)
• [2] Diego Collarana, Moritz Busch, Christoph Lange: Knowledge Graph Treatments for Hallucinating Large Language Models. ERCIM News 2024(136) (2024)
• [3] Junde Wu, Jiayuan Zhu, Yunli Qi: Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation. CoRR abs/2408.04187 (2024)
• [4] Sen, Priyanka, Sandeep Mavadia, and Amir Saffari. Knowledge graph-augmented language models for complex question answering. Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations - NLRSE (

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• 2023)

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• [5] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu: Unifying Large Language Models and Knowledge Graphs: A Roadmap. IEEE Trans. Knowl. Data Eng. 36 (7)

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• (2024)
• [6] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Jonathan Larson: From Local to Global: A Graph RAG Approach to Query-Focused Summarization. CoRR abs/2404.16130 (2024)
• [7] Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, Dhagash Mehta: HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction. CoRR abs/2408.04948 (2024)
• [8] Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi, Sahar Vahdati: Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources. Trans. Assoc. Comput. Linguistics

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• (2024)
• [9] Juan Sequeda, Dean Allemang, Bryon Jacob: A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. GRADES/NDA

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• (2024)

How do I enhance LLM explainability by using KGs? (2.2 – Answer Verification) – length: up to one page

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