CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
This addresses the problem of precise retrieval for LLM-augmented systems like GraphRAG, offering a domain-specific solution for knowledge graph integration.
The paper tackles the inefficiency of retrieving from modern RDF knowledge graphs like Wikidata for LLMs by proposing property graph views queried with Cypher, resulting in CypherBench, a benchmark with 11 large-scale graphs covering 7.8 million entities and over 10,000 questions.
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 (edge et al., 2024). 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.