mlsanityData to 14 Oct 2026

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

arXiv 2606.27742 ↗First posted 26 Jun 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

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-Cypher systems from existing KGs. KG2Cypher first constructs an executable Cypher query from observed graph facts and then uses LLMs to generate its associated natural-language question. The resulting Text-Cypher pairs are validated with an LLM judge and human validation, and are converted into candidate-aware SFT data. The trained generator is served with class-conditioned schema prompting, entity retrieval, and LoRA-based inference. We evaluate KG2Cypher in Korean enterprise settings, where short search-style queries and schema paraphrases make language grounding difficult. LoRA SFT improves execution-result F1 from 0.806 to 0.950 on broadcast-program queries and from 0.70 to 0.92 on company queries. In an 11-class setting, KG2Cypher achieves 95.2% exact match, 99.9% execution rate, and 0.964 execution-result F1.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 26 Jun 2026.

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References · 14

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  7. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  8. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  9. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  10. 2020Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Basescited by 171
  11. 2024Text2Cypher: Bridging Natural Language and Graph Databasescited by 30
  12. 2024Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification frameworkcited by 10
2 more
  1. 2025Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generationcited by 8
  2. 2025Text2Cypher Across Languages: Evaluating and Finetuning LLMscited by 3

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