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Xiaohongshu

Industry researchasia · cn
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Research library28linked papers
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Selected work

Representative Papers

A Structured Knowledge Infrastructure for Domain-Specific Data Asset Discovery

Jul 30, 2026

This work addresses the limitations of generic Retrieval-Augmented Generation (RAG) approaches in enterprise data analytics, which suffer from low retrieval accuracy (Hit@10 of only 19.1%) and misinterpretation of metrics due to semantic gaps, entity ambiguity, schema drift, and misalignment between data assets and their usage contexts. To overcome these challenges, the authors propose a dual-layer architecture: the lower layer constructs a three-tier, dual-purpose knowledge base integrating a knowledge graph with 2,859 nodes and eight-segment scenario annotations; the upper layer introduces a graph-guided retriever (GGR) and a scenario-aware ranker (SAR), enhanced by negative knowledge augmentation and a lightweight closed-loop hot-reload mechanism enabling daily knowledge updates. Evaluated on two hundred-question benchmarks, the approach achieves a Hit@10 of 96.6%, increases knowledge coverage to 77%, and maintains end-to-end latency between 4.84 and 5.33 seconds.

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Latest Papers

A Structured Knowledge Infrastructure for Domain-Specific Data Asset Discovery

Jul 30, 2026

This work addresses the limitations of generic Retrieval-Augmented Generation (RAG) approaches in enterprise data analytics, which suffer from low retrieval accuracy (Hit@10 of only 19.1%) and misinterpretation of metrics due to semantic gaps, entity ambiguity, schema drift, and misalignment between data assets and their usage contexts. To overcome these challenges, the authors propose a dual-layer architecture: the lower layer constructs a three-tier, dual-purpose knowledge base integrating a knowledge graph with 2,859 nodes and eight-segment scenario annotations; the upper layer introduces a graph-guided retriever (GGR) and a scenario-aware ranker (SAR), enhanced by negative knowledge augmentation and a lightweight closed-loop hot-reload mechanism enabling daily knowledge updates. Evaluated on two hundred-question benchmarks, the approach achieves a Hit@10 of 96.6%, increases knowledge coverage to 77%, and maintains end-to-end latency between 4.84 and 5.33 seconds.

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