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Probabilistic Reasoning with Undefined Properties in Ontologically-Based Belief Networks

Chia-Li Kuo, David Buchman, Arzoo Katiyar, David Poole

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2532-2538.

Abstract

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.

Chia-Li Kuo, David Buchman, Arzoo Katiyar, and David Poole. 2013. Probabilistic Reasoning with Undefined Properties in Ontologically-Based Belief Networks. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2532–2538. https://ijcai.org/proceedings/2013/373
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