Intelligent Retrieval Method for Geospatial Data Aimed at Data Trading

The development of the digital economy has highlighted the important value of geospatial data across numerous domains, with data trading being a pivotal link in activating this value. The current user base engaging in data trading is diverse, while trading platforms encounter problems such as disorganized data management and oversimplified retrieval methods. These concerns lead to inefficient retrieval for users with minimal domain knowledge. To address these complexities, this study proposes an intelligent retrieval method for geospatial data oriented toward data trading. This method establishes a geospatial data knowledge graph based on a standardized ontology model and innovatively utilizes large language models to assess user requirements in data trading. It effectively addresses the problems of standardized management for multi-source heterogeneous geospatial data and the poor adaptability of traditional retrieval methods to the ambiguous requirements of users lacking professional domain knowledge. Thus, it improves the efficiency and universality of geospatial data trading while guaranteeing the semantic interpretability of retrieval results. Experimental results confirm that the proposed method considerably outperforms traditional keyword-based retrieval methods. It exhibits particularly notable performance enhancements in scenarios with ambiguous requirements. This research not only effectively extends management approaches for geospatial data but also strengthens the inclusivity of data trading. Thus, it provides technical support for maximizing the value of geospatial data.