Learning OWL class expressions / Jens Lehmann.
With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representation and reasoning. However, recent progress in the field faces a lack of well-structured ontologies with large amounts of instance data due to the fact t...
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| Online Access: |
Full Text (via ProQuest) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Heidelberg :
AKA Akademische Verlagesellschaft : IOS Press,
2010.
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| Series: | Studies on the Semantic Web ;
v. 006. |
| Subjects: |
Table of Contents:
- 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 OperatorsA 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 CasesThe 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 LogicsRefenement 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.