Rule-Based Extraction of Spatial Relations in Natural Language Text

Intelligent geographical information systems (GISs) have been paid much attention in recent years, and the ultimate goal is to realize the natural language interaction between users and GISs. However, there is still a significant challenge for bridging the semantic gap between structured geospatial data in GISs and un-analytical spatial information in natural language. The representation and analysis of spatial relations has been one of generic issues in geographical information science. This paper presents a rule-based approach to spatial relation extraction in natural language text. Based on geographical named entity recognition technology and a spatial relation annotation corpus, syntactical rules of spatial relations are induced and then formalized into JAPE of the natural language processing platform GATE. Geographical named entities and spatial relations in new documents can be detected effectively in GATE. The experimental results indicate that spatial relations are usually described with several syntactical patterns in natural language, especially directional spatial relations, but topological relations are much more complicated. The fact is that rule-based extraction approaches can be implemented and integrated by means of fewer efforts than machine learning algorithms. It is known that directional spatial relations are more popularly used in natural language than topological spatial relations. Therefore, we conclude that it is practical and effective to extract spatial relations in natural language with rule-based approaches.