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CYPHERBENCH: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
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This pipeline automates the creation of a high-quality text-to-Cypher benchmark by first generating initial (question, Cypher) pairs from graph patterns using templates. LLMs are then employed in multiple stages to rewrite these template-generated questions into more natural-sounding language and to verify their semantic equivalence, effectively leveraging LLMs to generate text (questions) that describe facts or queries over KGs.
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This method proposes a novel approach to enable Large Language Models (LLMs) to efficiently and accurately retrieve information from full-scale modern RDF knowledge graphs. It involves transforming complex RDF graphs into domain-specific property graph views that are more amenable to LLM querying via Cypher, thereby enhancing the LLM's ability to access and utilize structured knowledge during inference.
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