Abstract
In this paper we introduce a framework for probabilistic reasoning on knowledge graphs. The framework leverages the notion of probabilistic knowledge graphs (PKGs), a dedicated probabilistic graphical model, as well as Soft Vadalog, a specific language for knowledge representation and reasoning on such model. We illustrate PKGs, the language and the general problem of probabilistic reasoning, providing approximate algorithmic tools to make it feasible and efficient. This work—a short version of our recent contribution to the International Joint Conference on Rules and Reasoning 2020—aims at making our results available to the broader statistical community.
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Bellomarini, L., Benedetto, D., Laurenza, E., Sallinger, E. (2023). A Framework for Probabilistic Reasoning on Knowledge Graphs. In: García-Escudero, L.A., et al. Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. https://doi.org/10.1007/978-3-031-15509-3_7
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