NALSpatial: A Natural Language Interface for Spatial Databases

Spatial databases play a vital role in a number of applications ranging from geographic information systems to location-based services. Application tasks typically access underlying spatial data to answer queries. However, non-experts lack the expertise necessary for formulating spatial queries. To...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 37; H. 4; S. 2056 - 2070
Hauptverfasser: Liu, Mengyi, Wang, Xieyang, Xu, Jianqiu, Lu, Hua, Tong, Yongxin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2025
Schlagworte:
Chatbots
Computational modeling
Data models
Distributed databases
Indexing
Natural language interface
Natural language processing
query processing
semantic parsing
Semantics
spatial database
Spatial databases
Structured Query Language
Translation
ISSN:1041-4347, 1558-2191
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Zusammenfassung:Spatial databases play a vital role in a number of applications ranging from geographic information systems to location-based services. Application tasks typically access underlying spatial data to answer queries. However, non-experts lack the expertise necessary for formulating spatial queries. To fill in this gap, we propose an effective framework that translates na tural l anguage queries over spatial data into executable database queries, called NALSpatial. The framework consists of two core phases: (i) natural language understanding and (ii) natural language translation . Phase (i) extracts key entity information, comprehends the query intent and determines the query type by employing natural language processing techniques and deep learning algorithms. The key entities and query type are passed to phase (ii), which makes use of entity mapping rules and structured language models to construct executable database queries. NALSpatial supports dealing with five types of queries including (i) basic queries (e.g. distance and area) , (ii) range queries , (iii) nearest neighbor queries , (iv) spatial join queries and (v) aggregation queries . We develop NALSpatial in an open-source extensible database system SECONDO. Extensive experiments show that NALSpatial on average achieves response time of about 2.5 seconds, translatability of 95% and translation precision of 92%, outperforming three state-of-the-art methods.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2025.3525587