Uncertainty in Artificial Intelligence
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Extending Term Subsumption systems for Uncertainty Management
John Yen, Piero Bonissone
Abstract:
A major difficulty in developing and maintaining very large knowledge bases originates from the variety of forms in which knowledge is made available to the KB builder. The objective of this research is to bring together two complementary knowledge representation schemes: term subsumption languages, which represent and reason about defining characteristics of concepts, and proximate reasoning models, which deal with uncertain knowledge and data in expert systems. Previous works in this area have primarily focused on probabilistic inheritance. In this paper, we address two other important issues regarding the integration of term subsumption-based systems and approximate reasoning models. First, we outline a general architecture that specifies the interactions between the deductive reasoner of a term subsumption system and an approximate reasoner. Second, we generalize the semantics of terminological language so that terminological knowledge can be used to make plausible inferences. The architecture, combined with the generalized semantics, forms the foundation of a synergistic tight integration of term subsumption systems and approximate reasoning models.
Keywords:
Pages: 468-474
PS Link:
PDF Link: /papers/90/p468-yen.pdf
BibTex:
@INPROCEEDINGS{Yen90,
AUTHOR = "John Yen and Piero Bonissone",
TITLE = "Extending Term Subsumption systems for Uncertainty Management",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "468--474"
}


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