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New benchmark evaluates natural language to graph query translation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Text2GraphQuery-Bench, a novel benchmark designed to evaluate systems that translate natural language into executable graph queries. This benchmark is the first to cover mainstream declarative property graph query languages including Cypher, GQL, and SQL/PGQ. It comprises over 267,000 question-query pairs across 34 databases and 13 domains, supporting adaptation and extension to new languages. AI

IMPACT This benchmark aims to improve the usability of graph databases by enabling natural language interfaces, potentially broadening access for non-technical users.

RANK_REASON The item is a research paper introducing a new benchmark for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark evaluates natural language to graph query translation

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The item is a research paper introducing a new benchmark for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Songlin Lyu, Lujie Ban, Zihang Wu, Tianqi Luo, Jirong Liu, Ayoub Moussaid, Oskar van Rest, Heng Lin, Chenhao Ma, Nan Tang, Shipeng Qi, Yongchao Liu, Zhan Qiu, Juelu Zhang, Jiajun Zheng ·

    Text2GraphQuery-Bench: A Text to Graph Query Benchmark

    arXiv:2602.11745v2 Announce Type: replace Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity, graph query languages are diverse (e.g., Cyphe…

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