Inference and Learning Systems for Uncertain Relational Data

The main objective of this dissertation is to propose approaches for reasoning and learning on uncertain relational data. The first part concerns reasoning over uncertain data. In particular, with regard to reasoning in PLP, we present the latest advances in the cplint system, which allows hybrid programs, i.e. programs where some of the random variables are continuous , and causal inference. The second part, which focuses on learning, considers two problems: parameter learning and structure learning.

We tested the proposed approaches on real-world datasets and their performance was comparable or superior to those of state-of-the-art systems.