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New framework EXYGEN enables LLMs to query knowledge graphs at scale

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EXYGEN, a framework designed to enable large language models (LLMs) to understand and query knowledge graphs (KGs) at scale through conversational interfaces. The system integrates metadata like VoID descriptions and ShEx schemas into a retrieval-augmented generation (RAG) pipeline, achieving a 0.419 exact match score on the SciQA benchmark without LLM fine-tuning. EXYGEN also introduces an efficient parallel graph sampling strategy to generate necessary metadata for extremely large KGs, reducing runtime by over 80x on datasets like OpenCitations Meta and GESIS. AI

IMPACT This research could significantly improve how LLMs interact with and extract information from large, complex knowledge graphs, potentially impacting fields reliant on structured data analysis.

RANK_REASON The cluster describes a new research paper detailing a framework for knowledge graph understanding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework EXYGEN enables LLMs to query knowledge graphs at scale

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The cluster describes a new research paper detailing a framework for knowledge graph understanding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Harshdeep Singh, Yurui Zhu, Giovanni Colavizza, Matteo Romanello ·

    Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)

    arXiv:2609.11569v1 Announce Type: cross Abstract: We present EXYGEN (EXplore Your Graphs ENgine), a framework for knowledge graph (KG) understanding that enables conversational access to KGs at scale. We address two questions in sequence. First, how effectively can LLMs perform t…

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