Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Frankenstein question answering architecture. We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions. The set of questions is offered to the research community as a gold standard dataset for the comparative evaluation of future geospatial question answering engines.

中文翻译：

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基于模板的对链接地理空间数据的问答

最近在链接的开放数据云和许多国家制图机构的门户网站上提供了大量地理空间数据（例如，OpenStreetMap 数据、各国的行政地理或土地覆盖/土地利用数据集）。这些数据集使用各种地理空间词汇，可以使用 SPARQL 或其 OGC 标准化扩展 GeoSPARQL 进行查询。在本文中，我们超越了这些方法，在链接的地理空间数据源之上为自然语言问题提供了一个问答引擎。我们的系统已被实现为科学怪人问答架构的可重用组件。我们使用一组 201 个自然语言问题详细描述了系统的架构、底层算法和评估。