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New pipeline KG2Cypher builds natural-language interfaces for enterprise knowledge graphs

Researchers have developed KG2Cypher, a data-centric pipeline designed to create natural-language interfaces for enterprise knowledge graphs. The system generates executable Cypher queries from graph facts and then uses LLMs to create corresponding natural-language questions. This process involves validation by an LLM judge and human review, with the resulting data used for supervised fine-tuning. KG2Cypher demonstrates significant improvements in query execution and accuracy, particularly in Korean enterprise settings. AI

IMPACT This research could streamline the creation of natural language interfaces for enterprise knowledge graphs, improving data accessibility and analysis.

RANK_REASON The cluster contains an academic paper detailing a new method for building text-to-Cypher systems.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New pipeline KG2Cypher builds natural-language interfaces for enterprise knowledge graphs

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Minjun Choi, Yerin Kim, Junghyuk Seo, Sujin Mo, Hyemin Lee, Youngjoong Ko ·

    KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems

    arXiv:2606.27742v1 Announce Type: cross Abstract: Enterprise Knowledge Graphs (KGs) are increasingly used for internal search, analytics, and question answering, but building natural-language interfaces for private enterprise graphs remains costly. We present KG2Cypher, a data-ce…

  2. arXiv cs.AI TIER_1 English(EN) · Youngjoong Ko ·

    KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems

    Enterprise Knowledge Graphs (KGs) are increasingly used for internal search, analytics, and question answering, but building natural-language interfaces for private enterprise graphs remains costly. We present KG2Cypher, a data-centric pipeline for building enterprise text-to-Cyp…