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Abstract: Speeding up inferences made from large knowledge bases is a key to scaling up knowledge based systems. To do so, a system must have the ability to automatically identify and ignore information that is irrelevant to a specific task. Identifying irrelevant knowledge is also key to enabling reasoning in environments in which several systems (and their respective knowledge bases) interoperate. This dissertation considers the problem of reasoning about irrelevance of knowledge in a principled and... (Update)
Context of citations to this paper: More
.... these object description components have many similarities with model fragments (MFs) the basic building blocks used in compositional modelling [Levy, 1993, 3 Container Contents Person P Oar:oar F M Portal SC SP Mass Thing mass size size propels mass part displaced holds...
...based on non conflicting assumptions. This approach has already been demonstrated in compositional modeling of physical systems [Levy, 1993]. 6 Methodology for Progress Consider taking some on line documentation that we would like to represent (e.g. the 777 hydraulics...
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BibTeX entry: (Update)
Levy, A. Y. 1993. Irrelevance Reasoning in Knowledge Based Systems. Ph.D. Dissertation, Stanford University. http://citeseer.ist.psu.edu/levy93irrelevance.html More
@phdthesis{ levy93irrelevance,
author = "A. Levy",
title = "Irrelevance Reasoning in Knowledge Based Systems",
school = "Stanford University",
year = "1993",
url = "citeseer.ist.psu.edu/levy93irrelevance.html" }
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