Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Web Semantics: Science, Services and Agents on the World Wide Web
Semantic Science: Ontologies, Data and Probabilistic Theories
Uncertainty Reasoning for the Semantic Web I
Ontology Design for Scientific Theories That Make Probabilistic Predictions
IEEE Intelligent Systems
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
On the role of context-specific independence in probabilistic inference
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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This paper concerns building probabilistic models with an underlying ontology that defines the classes and properties used in the model. In particular, it considers the problem of reasoning with properties that may not always be defined. Furthermore, we may even be uncertain about whether a property is defined for a given individual. One approach is to explicitly add a value "undefined" to the range of random variables, forming extended belief networks; however, adding an extra value to a random variable's range has a large computational overhead. In this paper, we propose an alternative, ontologically-based belief networks, where all properties are only used when they are defined, and we show how probabilistic reasoning can be carried out without explicitly using the value "undefined" during inference. We prove this is equivalent to reasoning with the corresponding extended belief network and empirically demonstrate that inference becomes more efficient.