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 (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.
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Yanlin Feng
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2024-12-30T20:03:01.909Z
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Yanlin Feng
user
yanlinf
6771fca3117cc54ff8b99a80
Simone Papicchio
extracted_confirmed
2024-12-31T15:09:48.747Z
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Simone PAPICCHIO
user
simone-papicchio
6771fca3117cc54ff8b99a81
Sajjadur Rahman
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 (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.
Property graph views and Cypher queries enable efficient knowledge retrieval from large RDF graphs for LLMs, improving performance on tasks like question answering.
knowledge graphs RDF LLMs retrieval GraphRAG property graphs Cypher Wikidata CypherBench evaluation metrics text-to-Cypher