Integrating Knowledge Graphs and Large Language Models for Querying in an Industrial Environment
Integrating Knowledge Graphs and Large Language Models for Querying in an Industrial Environment
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Knowledge graphs have traditionally required the use of specific query languages, such as SPARQL, to retrieve relevant data. In this paper, we present a system capable of performing natural language queries on knowledge graphs by leveraging retrievalaugmented generation (RAG) and large language models (LLMs). Oursystemcaningestlargeknowledgegraphsandanswerqueries using two approaches: first, by utilizing LLMs to extract information directly from subgraphs; and second, by generating SPARQL queries with LLMs and using the results to inform further inference, such as counting the number of items.