Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Learning probabilistic models of link structure
The Journal of Machine Learning Research
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Type uncertainty in ontologically-grounded qualitative probabilistic matching
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Semantic Science: Ontologies, Data and Probabilistic Theories
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The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
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LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
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ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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In probabilistic reasoning, the problems of existence and identity are important to many different queries; for example, the probability that something that fits some description exists, the probability that some description refers to an object you know about or to a new object, or the probability that an object fulfils some role. Many interesting queries reduce to reasoning about the role of objects. Being able to talk about the existence of parts and sub-parts and the relationships between these parts, allows for probability distributions over complex descriptions. Rather than trying to define a new language, this paper shows how the integration of multiple objects, ontologies and roles can be achieved cleanly. This solves two main problems: reasoning about existence and identity while preserving the clarity principle that specifies that probabilities must be over well defined propositions, and the correspondence problem that means that we don't need to search over all possible correspondences between objects said to exist and things in the world.