Title Page -- Acknowledgement -- Bibliographic Data -- Contents -- Chapter 1. Introduction -- Motivation -- Contributions -- Chapter Overview -- Chapter 2. Preliminaries and State of the Art -- Semantic Web -- History and Vision -- RDF and SPARQL -- Description Logics -- OWL -- Concept Learning and Inductive Reasoning -- History, Tools, and Applications -- Learning Problems in OWL/DLs -- Refinement Operators in OWL/DLs -- Chapter 3. Theoretical Foundations of Refinement Operators -- The Role of Minimality -- Combinations of Completeness, Properness, Finiteness, Redundancy -- Weak Completeness -- Chapter 4. Designing Refinement Operators -- A Complete OWL Refinement Operator -- Definition of the Operator -- Completeness of the Operator -- Achieving Properness -- Cardinality Restrictions and Concrete Role Support -- Optimisations -- An Ideal EL Refinement Operator -- EL Trees and Simulation Relations -- Formal Description of the EL Refinement Operator -- Operator Performance -- Chapter 5. Refinement Operator Based OWL Learning Algorithms -- OCEL (OWL Class Expression Learner) -- Redundancy Elimination -- Creating a Full Learning Algorithm -- ELTL (EL Tree Learner) -- CELOE (Class Expression Learner for Ontology Engineering) -- Chapter 6. Improving Scalability of OWL Learning Algorithms -- The DBpedia Project -- The DBpedia Knowledge Extraction Framework -- The DBpedia Knowledge Base -- Interlinked Web Content -- Applications -- Knowledge Fragment Selection -- What Properties Should the Fragment Have? -- Extending Concise Bound Descriptions (CBDs) -- Extraction Methods -- OWL DL Conversion of the Fragment -- SPARQL Implementation of Tuple Acquisition -- Usage Scenarios -- Optimising Coverage Tests -- Approximate and Partial Closed World Reasoning -- Stochastic Coverage Computation -- Chapter 7. Implementation, Evaluation, and Use Cases.

The DL-Learner Project -- ILP Learning Problems -- Comparison with other Algorithms based on Description Logics -- Comparison with other ILP approaches -- Ontology Engineering -- The Protege Plugin -- The OntoWiki Plugin -- Evaluation of CELOE -- Fragment Extraction Evaluation -- Further Applications -- Predictions of the Effect of Mutations on the Protein Function -- NLP2RDF -- ORE - Ontology Repair and Enrichment -- moosique.net - Music Recommendations -- Strengths and Limitations of the Described Approaches -- Chapter 8. Related Work -- Inductive Learning in Description Logics -- Refenement Operators -- (Semi-)Automatic Ontology Engineering -- Knowledge Fragment Selection -- Chapter 9. Conclusions and Future Work -- Refwnement Operators -- Learning Algorithms and Scalability -- Implementation, Evaluation and Use Cases -- Future Work -- Chapter A. Software Release History -- Chapter B. DL-Learner Manual -- What is DL-Learner? -- Getting Started -- DL-Learner Architecture -- DL-Learner Components -- Knowledge Sources -- Reasoner Components -- Learning Problems -- Learning Algorithms -- DL-Learner Interfaces -- Extending DL-Learner -- General Information -- Chapter C. Curriculum Vitae -- Related Peer Reviewed Publications -- Other Publications -- Talks -- Research Projects and Groups -- Programm Committee, Reviewing -- Seminars and Teaching -- Supervision -- List of Tables -- List of Figures -- List of Algorithms -- List of Definitions -- List of Theorems -- List of Examples and Remarks -- Bibliography -- Selbstandigkeitserklarung.