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Probabilistic Knowledge Representation in Machine Learning – AI*IA 2018 Tutorial

Authors

Elena Bellodi
Dipartimento di Ingegneria
Università di Ferrara

Riccardo Zese
Dipartimento di Ingegneria
Università di Ferrara

Abstract

Representing uncertain information is crucial for modeling current real-world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the recent introduction of Probabilistic Logic Languages in logic and with various probabilistic extensions of DLs respectively.
Machine learning approaches based on the combination of logic and probability have originated the field of Statistical Relational Artificial Intelligence (StarAI), which is getting an increasing attention.
In fact, probabilistic languages based on Logic Programming (LP) are particularly promising because of the large body of techniques for inference and learning developed in LP. On the other hand, Probabilistic Description Logics (PDL) can meet the Semantic Web need of representing and reasoning on structured but often incomplete data by exploiting knowledge representation formalisms that possess nice computational properties such as decidability and/or low complexity.

Slides

Part 1 – Probabilistic Logic Programming
Part 2 – Probabilistic Description Logics and Beyond