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Intro -- Title Page -- Abstract -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- List of Acronyms -- Introduction -- Motivation -- Aims of the Thesis -- Structure of the Thesis -- Structure -- Thesis Contributions -- Inference in Probabilistic Logic Programming -- Inference in Probabilistic Description Logics -- Learning Systems in Probabilistic Logic Programming -- Learning Systems in Probabilistic Description Logics -- How to read this thesis -- Probabilistic Logics -- Fundamentals of First-Order Logic and Logic Programming -- Introduction -- First-Order Logic -- Syntax -- Tarski''s semantics -- Logic Programming -- Prolog -- Normal Logic Programs -- First-Order Logic vs Logic Programs -- Conclusions -- Distribution Semantics -- Introduction -- Formal Definition -- Conclusions -- Probabilistic Logic Programming Languages -- Introduction -- Logic Programs with Annotated Disjunctions -- LPADs Syntax -- LPADs Semantics -- ProbLog -- ProbLog Syntax -- Conclusions -- Description Logics and OWL -- Introduction -- Description Logics -- Syntax -- Concept and Role Constructors -- Concept Constructors -- Role constructors -- Knowledge Base -- Nomenclature -- Semantics -- Decidability of Description Logics -- Description Logics and First-Order Logic -- The OWL Ontology Language -- OWL Syntax -- OWL sublanguages -- Tools for OWL -- Conclusions -- Reasoning in Description Logics -- Reasoning Problems -- Closed vs Open World Assumption -- Reasoning Techniques -- Pellet -- Tableau Algorithm -- Explanation finding -- Pinpointing formula -- Conclusions -- Probabilistic Description Logics -- Introduction -- The Distribution Semantics for Description Logics: DISPONTE -- Syntax -- Semantics -- Assumption of Independence -- Related Work -- Conclusions -- Inference in Probabilistic Logics -- Decision Diagrams -- Introduction -- Multivalued Decision Diagrams -- Binary Decision Diagrams -- Conclusions -- Fundamentals of Exact Probabilistic Logical Inference -- Inference Approaches -- Exact Probabilistic Logical Inference -- Splitting Algorithm -- Inference with Multi-valued Decision Diagrams -- Inference with Binary Decision Diagrams -- Conclusions -- Inference in Probabilistic Logic Programming -- Introduction -- cplint -- Exact Inference: the PITA module -- Approximate Inference: the MCINTYRE module -- Causal Inference with cplint -- Causal Inference in PLP -- Causal Exact Inference with cplint -- Causal Approximate Inference with cplint -- Notable Examples -- Simpson''s Paradox -- Viral Marketing -- Experiments -- Hybrid Probabilistic Logic Programs with cplint -- Sampling the Arguments of Unconditional Queries over Hybrid Programs -- Conditional Queries over Hybrid Logic Programs -- cplint on SWISH: a Web interface for cplint -- SWISH -- cplint on SWISH -- Examples -- Related Work -- Work on causality inference -- Work on Hybrid Probabilistic Logic Programs -- Web application for PLP -- Conclusions -- Inference in Probabilistic Description Logics -- Introduction -- BUNDLE -- How to use BUNDLE -- TRILL -- TRILLP -- How to use TRILL and TRILLP -- TRILL on SWISH -- Inference Complexity -- Experiments -- Comparing the Systems -- Related Work -- Conclusions -- Learning -- Introduction to Statistical Relational Learning -- Introduction -- Inductive Logic Programming -- Statistical Relational Learning -- Parameter Learning -- Structure Learning -- Conclusions -- Distributed Learning in Probabilistic Logic Programming -- Introduction -- Parameter Learning: EMBLEM -- Structure Learning: SLIPCOVER -- Distributed Parameter Learning: EMBLEMMR -- Distributed Structure Learning: SEMPRE -- Experiments -- Conclusions -- Parameter Learning in Probabilistic Description Logics -- Introduction -- EDGE -- Expectation Computation -- EDGE''s Algorithm -- How to Use EDGE -- Conclusions -- Distributed Parameter Learning for Probabilistic Description Logics -- Introduction -- Distributed Parameter Learning: EDGEMR -- MapReduce View -- Scheduling Techniques -- EDGEMR''s Algorithm -- Experiments -- Conclusions -- Structure Learning in Probabilistic Description Logics -- Introduction -- The Learning Problem -- Refinement Operators in Description Logics -- CELOE -- DL-Learner -- Structure Learning: LEAP -- Architecture -- Interfacing CELOE and EDGE -- LEAP -- Related Work -- Experiments -- Conclusions -- Distributed Structure Learning in Probabilistic Description Logics -- Distributed Structure Learning: LEAPMR -- Experiments -- Conclusions -- Conclusions and Future Work -- Conclusions -- Future Work -- Future Work on Inference -- Future Work on Learning -- Bibliography -- .