Formalizing natural-language spatial relations between linear objects with topological and metric properties

People usually use qualitative terms to express spatial relations, while current geographic information systems (GISs) all use quantitative approaches to store spatial information. The abilities of current GISs to represent and query spatial information about geographic space are limited. Based on the result of a humansubject test of natural-language descriptions of spatial relations between linear geographic objects, this paper defines a series of quantitative indices that are related to natural-language spatial relation terms, and uses these indices to formalize the ambiguous natural-language representation with a decision-tree algorithm. The result indicates that using both topological indices and metric indices can formalize the natural-language spatial predicates better than using only topological indices. The rules extracted from the trees are used to characterize the spatial relations into qualitative description groups. Using these rules, a prototype of an intelligent natural-language interface for the ESRI software ArcGIS that can query spatial relations between two linear objects in natural English language is implemented using SNePS (the Semantic Network Processing System).