An On-Line Algorithm for Semantic Forgetting
Packer, Heather Stephanie (University of Southampton) | Gibbins, Nicholas (University of Southampton) | Jennings, Nicholas R (University of Southampton)
In AI, this area Ontologies that evolve through use to support new has been studied under a variety of names such as forgetting domain tasks can grow extremely large. Moreover, and variable elimination [Eiter et al., 2006; Wang et al., large ontologies require more resources to use and 2008]. We provide a general approach for ranking knowledge have slower response times than small ones. To according to its use and cost, which can be applied to systems help address this problem, we present an online semantic that are limited by memory resources to evaluate memory forgetting algorithm that removes ontology allocation. We also provide a specific approach to select fragments containing infrequently used or cheap to which concepts to remove from an ontology, using the ranking.
Jul-19-2011
- Country:
- Asia
- Europe
- Hungary > Budapest
- Budapest (0.04)
- Italy > Emilia-Romagna
- Metropolitan City of Bologna > Bologna (0.04)
- Spain > Canary Islands
- Tenerife (0.04)
- United Kingdom > Scotland
- City of Edinburgh > Edinburgh (0.04)
- Hungary > Budapest
- North America
- Canada > Ontario
- Toronto (0.04)
- United States
- District of Columbia > Washington (0.04)
- New York (0.04)
- Canada > Ontario
- Genre:
- Research Report (0.46)
- Industry:
- Technology: