Metric details for natural-language spatial relations
ACM Transactions on Information Systems (TOIS)
Language and Spatial Cognition
Language and Spatial Cognition
A model for describing and composing direction relations between overlapping and contained regions
Information Sciences: an International Journal
International Journal of Geographical Information Science
Annotation of Spatial Relations in Natural Language
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03
RCC8 binary constraint network can be consistently extended
Artificial Intelligence
The influence of scale, context and spatial preposition in linguistic topology
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
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Spatial relation terms can generally indicate spatial relations described in natural language context. Their semantic representation is closely related to geographical entities and their characteristics e.g. geometry, scale and geographical feature types. This paper proposes a quantitative approach to explore the semantic relevance of spatial relation terms and geographical feature types in text. Firstly, a classification of spatial relation terms is performed. Secondly, the "Overlap" similarity measure is introduced to define the relevance of spatial relation terms and geographical feature types based on a large scale annotation corpus. Thirdly, the relevance is expanded with the semantic distance and hierarchical relationship of the classification system of geographical feature types. Finally, a knowledge base based on protégé is developed to formally represent and visualize geographical feature types, spatial relation classifications, and the relevance of spatial relation terms and geographical feature types. This study indicates that spatial relation terms are strongly relevant to geographical feature types. The semantic representation of topological relation terms is diverse and their relevance with geographical feature types is much stronger than directional relation and distance relation terms, but the annotation quality and the classification granularity of geographical entities in the corpus have a great effect on the performance.