Abstract
Identity uncertainty is the task of deciding whether two descriptions correspond to the same object. In this paper we discuss the identity uncertainty problem in the context of the person identity uncertainty problem – the problem of deciding whether two descriptions refer to the same person. We model the inter-dependence of the attributes using a similarity network representation. We present results that show that our method outperforms the traditional approach for person identity uncertainty which considers the attributes as independent of each other.
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Sharma, R., Poole, D. (2005). Probability and Equality: A Probabilistic Model of Identity Uncertainty. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_24
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DOI: https://doi.org/10.1007/11424918_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
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