Abstract
Standard approaches for inference in probabilistic relational models include lifted variable elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model, employing LVE as a subroutine in its steps. LVE and LJT can only handle certain evidence. However, most events are not certain. The purpose of this paper is twofold, (i) to adapt LVE, presenting LVE\(^{evi}\), to handle uncertain evidence and (ii) to incorporate uncertain evidence for multiple queries in LJT, presenting LJT\(^{evi}\). With LVE\(^{evi}\) and LJT\(^{evi}\), we can handle uncertain evidence for probabilistic relational models, while benefiting from the lifting idea. Further, we show that uncertain evidence does not have a detrimental effect on completeness results and leads to similar runtimes as certain evidence.
This research originated from the Big Data project being part of Joint Lab 1, funded by Cisco Systems Germany, at the centre COPICOH, University of Lübeck.
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Gehrke, M., Braun, T., Möller, R. (2019). Uncertain Evidence for Probabilistic Relational Models. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_7
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