LLM-Assisted Metadata Query Answering on Data Warehouses

Large Language Models (LLMs) are increasingly adopted to support user interaction with complex data management systems. While recent research has explored their use in tasks such as multidimensional design and translation of analytical queries from natural language into MDX, little attention has been devoted to assist users in querying data warehouse (DW) metadata. In this paper, we investigate the use of LLMs to answer natural language queries on a DW schema. To this end, we rely on a knowledge graph (KG) that represents DW metadata according to the Dimensional Fact Model. We define a benchmark of queries organized into four categories and evaluate the ability of GPT to answer them, comparing them to the baseline capabilities offered by a CASE tool for DW design. We also assess the benefits of enriching the KG with natural language definitions and descriptions. The results show that LLMs can significantly improve the accessibility of DW metadata, while the accuracy varies depending on the query type and the level of semantic enrichment provided.