  This thesis focuses on advancing probabilistic logic programming (PLP), which
combines probability theory for uncertainty and logic programming for
relations. The thesis aims to extend PLP to support both discrete and
continuous random variables, which is necessary for applications with numeric
data. The first contribution is the introduction of context-specific likelihood
weighting (CS-LW), a new sampling algorithm that exploits context-specific
independencies for computational gains. Next, a new hybrid PLP, DC#, is
introduced, which integrates the syntax of Distributional Clauses with Bayesian
logic programs and represents three types of independencies: i) conditional
independencies (CIs) modeled in Bayesian networks; ii) context-specific
independencies (CSIs) represented by logical rules, and iii) independencies
amongst attributes of related objects in relational models expressed by
combining rules. The scalable inference algorithm FO-CS-LW is introduced for
DC#. Finally, the thesis addresses the lack of approaches for learning hybrid
PLP from relational data with missing values and (probabilistic) background
knowledge with the introduction of DiceML, which learns the structure and
parameters of hybrid PLP and tackles the relational autocompletion problem. The
conclusion discusses future directions and open challenges for hybrid PLP.
