CN-PDS: Facilitating Dataset Search with LLMs and its Application to China’s Public Data

Open data plays a critical role in advancing social good, yet access to this open data resource remains fragmented, with different publishers hosting data across various decentralized portals. This decentralization poses significant challenges for building unified search systems, primarily due to heterogeneity and, often, the low quality of metadata across these portals. In this paper, we address these challenges within the context of public dataset search by exploring the application of large language models (LLMs) in a zero-shot or few-shot manner to consolidate and enrich heterogeneous and incomplete metadata. We also investigate how LLMs can be utilized to rerank retrieved datasets, thereby enhancing search accuracy, and to provide relevance explanations to help users make more informed decisions. Using these techniques, we develop the China Public Data Search system (CN-PDS), a unified dataset search system that integrates datasets from public data portals of various provinces and cities in China. Through extensive experiments on China’s public data and other standard test collections for dataset search, we validate the effectiveness of our approach. Our empirical evaluation, coupled with the insights gained from this work, offers valuable lessons in improving real-world dataset search systems using LLMs.