CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era

Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system [1] . Despite decades of research on knowledge graphs and knowledge base question answering, leading LLM frameworks (e.g., Langchain and LlamaIndex) have only minimal support for retrieval from modern encyclopedic knowledge graphs like Wikidata. In this paper, we analyze the root cause and suggest that modern RDF knowledge graphs (e.g., Wikidata, Freebase) are less efficient for LLMs due to overly large schemas that far exceed the typical LLM context window, use of resource identifiers, overlapping relation types and lack of normalization. As a solution, we propose property graph views on top of the underlying RDF graph that can be efficiently queried by LLMs using Cypher. We instantiated this idea on Wikidata and introduced CypherBench, the first benchmark with 11 large-scale, multi-domain property graphs with 7.8 million entities and over 10,000 questions. To achieve this, we tackled several key challenges, including developing an RDF-to-property graph conversion engine, creating a systematic pipeline for text-to-Cypher task generation, and designing new evaluation metrics. Dataset https://huggingface.co/datasets/megagonlabs/cypherbench Code https://github.com/megagonlabs/cypherbench * The work began during Simone Papicchio's internship at Megagon Labs. As part of one subtask of his overall internship goal, he implemented an initial version of the benchmark that involved SQL-inspired template design, query categorization, and validation of the generated benchmark. The work has since further evolved to broaden and bolster the template generation process and redefining query categories while introducing new evaluation metrics. 2 Graph retrieval can be considered as a broader task than KBQA, as it is not only essential for question answering but also for other tasks such as fact checking [9] .