Template-Based Question Answering over Linked Geospatial Data
This addresses the challenge of querying diverse geospatial datasets for users without technical expertise, though it is incremental as it builds on existing question-answering architectures.
The paper tackles the problem of enabling natural language question answering over linked geospatial data, and the result is a system evaluated on 201 questions, with the question set released as a gold standard dataset for future research.
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.