CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
The spatial semantic hierarchy
Artificial Intelligence
A Hybrid Location Model with a Computable Location Identifier for Ubiquitous Computing
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Reviewing the design of DAML+OIL: an ontology language for the semantic web
Eighteenth national conference on Artificial intelligence
Using Semantic Networks for Knowledge Representation in an Intelligent Environment
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Representing Knowledge of Large-scale Space
Representing Knowledge of Large-scale Space
Semantic Web in the Context Broker Architecture
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
A location representation for generating descriptive walking directions
Proceedings of the 10th international conference on Intelligent user interfaces
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Current location representations model only the geographical aspects of a place. While this is a necessary feature to capture, it is far from sufficient. As a result, many location-aware applications reason about space at the level of coordinates and containment relationships, but have no means to express the semantics that define how a particular space is used. The latter is particularly important in modeling location in the pervasive computing domain. We present LAIR, an ontology that addresses this problem by modeling both the geographical and topological relationships between spaces, as well as the functional purpose of a given space.