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;
Book — 1 online resource (ix, 173 pages) : illustrations Digital: text file.PDF.
Derivation reduction of metarules in meta-interpretive learning.- Large-Scale Assessment of Deep Relational Machines.- How much can experimental cost be reduced in active learning of agent strategies?.- Diagnostics of Trains with Semantic Diagnostics Rules.- The game of Bridge: a challenge for ILP.- Sampling-Based SAT/ASP Multi-Model Optimization as a Framework for Probabilistic Inference.- Explaining Black-box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules.- Learning Dynamics with Synchronous, Asynchronous and General Semantics.- Was the Year 2000 a Leap Year? Step-wise Narrowing Theories with Metagol.- Targeted End-to-end Knowledge Graph Decomposition.
(source: Nielsen Book Data)
This book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. (source: Nielsen Book Data)