NALSpatial: An Effective Natural Language Transformation Framework for Queries over Spatial Data

Spatial databases play a vital role in many applications that access spatial data via appropriate queries. However, most application users lack the expertise necessary for formulating spatial queries. To fill in this gap, we propose an effective framework called NALSpatial that translates natural language queries over spatial data into executable database queries. NALSpatial consists of two core phases. The natural language understanding phase extracts key entity information, comprehends the query intent and determines the query type. The key entities and query type are passed to the subsequent natural language translation phase, which employs entity mapping rules and structured language models to construct executable database queries accordingly. We implement NALSpatial on the open-source extensible database system SECONDO to support range queries, nearest neighbor queries, spatial joins and aggregation queries. Extensive experiments show that NALSpatial on average achieves response time of about 2.5 seconds, translatability of 95% and translation precision of 92%, outperforming state-of-the-art natural language transformation methods.