A general formulation of conceptual spaces as a meso level representation
Artificial Intelligence
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
A concept geometry for conceptual spaces
Fuzzy Optimization and Decision Making
Reasoning about categories in conceptual spaces
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Similarity measurement in context
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Spatial semantics in difference spaces
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
Affordance-based similarity measurement for entity types
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
Measuring semantic similarity between geospatial conceptual regions
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Semantic referencing - determining context weights for similarity measurement
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Constructing geo-ontologies by reification of observation data
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Geospatial semantics and linked spatiotemporal data --Past, present, and future
Semantic Web - On linked spatiotemporal data and geo-ontologies
Hi-index | 0.00 |
The modeling of concepts from a cognitive perspective is important for designing spatial information systems that interoperate with human users. Concept representations that are built using geometric and topological conceptual space structures are well suited for semantic similarity and concept combination operations. In addition, concepts that are more closely grounded in the physical world, such as many spatial concepts, have a natural fit with the geometric structure of conceptual spaces. Despite these apparent advantages, conceptual spaces are underutilized because existing formalizations of conceptual space theory have focused on individual aspects of the theory rather than the creation of a comprehensive algebra. In this paper we present a metric conceptual space algebra that is designed to facilitate the creation of conceptual space knowledge bases and inferencing systems. Conceptual regions are represented as convex polytopes and context is built in as a fundamental element. We demonstrate the applicability of the algebra to spatial information systems with a proof-of-concept application.