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Selected papers from the international workshop on Uncertainty in databases and deductive systems
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Artificial Intelligence
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HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
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Future Generation Computer Systems - Special issue: Semantic grid and knowledge grid: the next-generation web
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CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
The Knowledge Grid Based Intelligent Electronic Commerce Recommender Systems
SOCA '07 Proceedings of the IEEE International Conference on Service-Oriented Computing and Applications
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WoLLIC '08 Proceedings of the 15th international workshop on Logic, Language, Information and Computation
Semantics and Knowledge Grids: Building the Next-Generation Grid
IEEE Intelligent Systems
Uncertainty Reasoning for the Semantic Web I: ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers
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Journal of Artificial Intelligence Research
Probabilistic inductive logic programming
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
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Automated computing in open and dynamic computing environments requires automatic update and revision of the Knowledge Bases (KBs) to keep the KBs up to date with the dynamics in the environment and correct incorrect knowledge held in the KBs respectively. Furthermore, the truthfulness, applicability and validity of this knowledge depend on the context under which the knowledge is to be used. This then calls for the development of solutions to enable KBs to (i) be evolved over time enabling them to keep up to date with the evolving world or changes in the world's conceptualisation, (ii) allow situational reasoning, and (iii) reasoning under uncertain, incomplete and inconsistent knowledge. The emerging fielding of probabilistic ontologies is impregnated with promises to resolve such issues. However, an investigation on how such knowledge representations can be objectively and rationally evolved is needed. This paper presents issues, methods and ideas towards rational probabilistic ontology evolution in open and dynamic computing environments.