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• Dense and sparse vectors (https://infiniflow.org/blog/best-hybrid-search-solution, https://aclanthology.org/2023.findings-acl.679.pdf) 
• Hybrid Retrieval (https://arxiv.org/html/2408.05141v1, https://haystack.deepset.ai/blog/hybrid-retrieval, https://arxiv.org/pdf/1905.07129) 
• Graph Embeddings (https://www.dfki.de/~declerck/semdeep-4/papers/SemDeep-4_paper_2.pdf, https://arxiv.org/pdf/1711.11231)
• Re-ranking, scoring, and filtering by fusion (https://www.elastic.co/blog/improving-information-retrieval-elastic-stack-hybrid, https://arxiv.org/pdf/2004.12832, https://arxiv.org/pdf/2009.07258) 
• Integration of KG with dense vectors (https://github.com/InternLM/HuixiangDou) 
• Benefits (enhance semantic understanding, contextual and structure insights, improve retrieval accuracy) 
• Challenges (scalability, integration complexity) https://ragaboutit.com/how-to-build-a-jit-hybrid-graph-rag-with-code-tutorial/

Key Challenges

• 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 (2023)
• [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) (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 (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 (2024)

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