Title Page -- Contents -- Part I. Introduction -- Chapter 1. Semantic Web -- 1.1 Description Logics and Semantic Web -- 1.2 The Current Vision of the Semantic Web -- Chapter 2. Probability -- 2.1 Probabilistic Inference -- 2.2 Probabilistic Learning -- Chapter 3. Aims of the Thesis -- Chapter 4. Structure of the Thesis -- Part II. Description Logics -- Chapter 5. Foundations of Description Logics -- Chapter 6. Description Logics' Characteristics -- 6.1 Concept and Role Constructors -- 6.2 Family of DLs -- 6.3 Knowledge Base -- 6.3.1 TBox -- 6.3.2 RBox -- 6.3.3 ABox -- 6.4 Semantics -- Chapter 7. Significant Examples of Description Logics -- Chapter 8. OWL: the Web Ontology Language -- Chapter 9. Inference in Description Logics -- 9.1 Approaches to Compute Explanations -- 9.1.1 Solving min-a-enum: The Standard Definition -- 9.1.2 Resolving min-a-enum: Pinpointing Formula -- Part III. A Probabilistic Semantics for Description Logics -- Chapter 10. Distribution Semantics -- 10.1 Formal Definition -- 10.2 PLP Languages under the Distribution Semantics -- 10.2.1 Logic Programming -- 10.2.2 LPAD -- 10.2.3 ProbLog -- 10.3 Inference in Probabilistic Logic Programming -- 10.3.1 ProbLog Inference System -- 10.3.2 PITA -- 10.4 Learning in Probabilistic Logic Programming -- Chapter 11. DISPONTE -- Chapter 12. Probabilistic Description Logics -- Part IV. Inference in Probabilistic DLs -- Chapter 13. Inference -- 13.1 Splitting Algorithm -- 13.2 Binary Decision Diagrams -- Chapter 14. BUNDLE -- Chapter 15. TRILL -- 15.1 TRILL on SWISH -- Chapter 16. TRILL P -- Chapter 17. Complexity of Inference -- Chapter 18. Related Inference Systems -- Chapter 19. Experiments -- 19.1 BUNDLE: Comparison with PRONTO -- 19.2 BUNDLE: Not Entailed Queries -- 19.3 BUNDLE: Inference with Limited Number of Explanations -- 19.4 BUNDLE: Scalability