An On-Line Algorithm for Semantic Forgetting
Heather S. Packer, Nicholas Gibbins, Nicholas R. Jennings
Ontologies that evolve through use to support new domain tasks can grow extremely large. Moreover, large ontologies require more resources to use and have slower response times than small ones. To help address this problem, we present an on-line semantic forgetting algorithm that removes ontology fragments containing infrequently used or cheap to relearn concepts. We situate our algorithm in an extension of the widely used RoboCup Rescue platform, which provides simulated tasks to agents. We show that our agents send fewer messages and complete more tasks, and thus achieve a greater degree of success, than other state-of-the-art approaches.