An Emergent Ontology for Ambient Intelligence based on an Ant Colony Optimization algorithm

An Ambient Intelligence (AmI) requires a conceptual definition of its components, its devices must handle a common semantic for reasoning about context and specific application domains. Ontology is an ideal tool for the semantic characterization of AmI. In that sense, update and evolution of each ontology should be in the same moment when information changes in the environment and the application domain also. Context information can provide specific data on a new object in the environment, to characterize and to classify it within the ontology. For this reason, we propose an "Emergent Ontology” based on an Ant Colony Optimization algorithm to overcome the need for an emergent and dynamic semantic for an AmI. This proposal emergent ontology is structured according to three ontologies to evolve in real time: context ontology, another associated with AmI\´s components, and the last one, about conceptual model of a particular domain.