The example attributes in the metadata tables.

Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data from a drop-down manual from the user interface. However, the search results provide limited information about the data product, where detailed descriptions, potential use, and relationship with other data products are still missing. Language models (LMs) have demonstrated great potential in tasks like question answering, sentiment analysis, text classification, and machine translation. However, they struggle when dealing with metadata represented in tabular format. To overcome these challenges, we developed Meta Question Answering System (MetaQA), a novel spatial data search model. MetaQA integrates end-to-end AI models with a generative pre-trained transformer (GPT) to enhance geosearch services. Using GCOOS metadata as a case study, we tested the effectiveness of MetaQA. The results revealed that MetaQA outperforms state-of-the-art question-answering models in handling tabular metadata, underlining its potential for user-inspired geosearch services.

获取并利用多源地理空间数据，对于开展旨在解决需跨学科知识支撑的复杂科学与社会挑战的科研工作而言至关重要。传统基于关键词的地理搜索方法存在局限性，这是由于空间信息本身固有的不确定性，以及其在数据共享平台中的呈现方式所导致的。例如，墨西哥湾沿岸海洋观测系统（Gulf of Mexico Coastal Ocean Observing System, GCOOS）数据搜索平台将地理信息与元数据存储于复杂的表格格式中，用户可通过输入关键词，或从用户界面的下拉菜单中选择数据的方式进行检索。然而，该平台的搜索结果仅能提供关于数据产品的有限信息，数据产品的详细说明、潜在用途以及与其他数据产品的关联关系均未提及。语言模型（Language Models, LMs）已在问答、情感分析、文本分类与机器翻译等诸多任务中展现出巨大应用潜力，但这类模型在处理表格格式的元数据时却存在性能瓶颈。为克服上述挑战，我们研发了元数据问答系统（Meta Question Answering System, MetaQA），这是一种新型空间数据搜索模型。MetaQA将端到端人工智能模型与生成式预训练Transformer（Generative Pre-trained Transformer, GPT）相集成，以提升地理搜索服务的性能。我们以GCOOS元数据作为案例研究，对MetaQA的有效性进行了测试。实验结果表明，在处理表格格式元数据方面，MetaQA的性能优于当前顶尖的问答模型，这凸显了其在面向用户需求的地理搜索服务中的应用潜力。