A Semantics-Guided and Spatial-Aware Framework for Natural Resources Geo-Analytical Question Answering

Question answering system is an emerging information service system, which enables people to get answers easily and quickly to their questions. However, most of the existing methods do not effectively utilize semantic information and spatial properties. In this paper, a semantics-guided and spatial-aware framework for natural resources geo-analytical question answering is proposed. First, we use linguistic analysis techniques to translate natural language questions into structured texts. Then, the constructed natural resources semantic knowledge bases and query sample bases are introduced to extract the spatial and semantic information of geographic entities (i.e., precise geographic location and spatial representation). Considering most questions need to be answered based on a combination of multiple data sources, an improved user model that introduces semantic similarity is proposed to select appropriate data sources for a specific question. Finally, geo-analytical workflows are automatically generated by utilizing graph models and rule templates. Additionally, a question answering system was developed based on this framework and applied to natural resources monitoring in Hubei. The proposed framework is validated in both experiments and the case study. The results show that the proposed framework performs favorably on natural resources geo-analytical question answering tasks.