The Semantic Web - ISWC 2017 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I
- 1 online resource (806 pages)
- Lecture Notes in Computer Science Ser. v.10587 .
Description based on publisher supplied metadata and other sources
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- From Relational to Semantic Data Mining -- Ontologies for the Modern Age -- Applied Semantics: Beyond the Catalog -- Contents - Part I -- Contents -- Part II -- Research Track -- Multi-label Based Learning for Better Multi-criteria Ranking of Ontology Reasoners -- 1 Introduction -- 2 Background and Related Works -- 2.1 Key Notions of Multi-label Learning Paradigm -- 2.2 Multi-label Learning Techniques for Algorithm Selection -- 2.3 Ontology Features -- 3 Novel Multi-label Learning Method for Multi-criteria Ranking of Ontology Reasoners -- 3.1 Reasoner Ranking Criteria and Preference Rules -- 3.2 Specification of the Novel Multi-label Ranking Method -- 3.3 Multi-RakSOR Learning and Prediction Steps -- 3.4 Ranking Consistency Checking Method -- 4 Data Collection -- 5 Experimental Evaluation of Multi-RakSOR -- 5.1 Evaluation Metrics -- 5.2 Multi-label Learning Methods -- 5.3 Relevance Prediction Assessment Results -- 5.4 Ranking Prediction Assessment Results -- 6 Experimental Evaluation of Meta-RakSOR -- 7 Conclusion -- References -- The Efficacy of OWL and DL on User Understanding of Axioms and Their Entailments -- 1 Introduction -- 2 Background -- 3 Tasks: Understanding Axioms and Inference -- 4 Main Hypotheses -- 5 Empirical Study Design -- 5.1 Designing Questions for the Study -- 5.2 Experiment Phases -- 5.3 Experiment Execution -- 5.4 Statistical Methods -- 6 Results -- 6.1 Understanding Tasks -- 6.2 Sound Inference Tasks -- 6.3 Unsound Inference Tasks -- 6.4 Time Performance -- 7 Discussion -- 7.1 Understanding Axioms -- 7.2 Sound Inferences -- 7.3 Unsound Inferences -- 8 Threats to Validity -- 9 Conclusion -- References -- A Decidable Very Expressive Description Logic for Databases -- 1 Introduction -- 2 The Description Logic DLR+ -- 3 Expressiveness of DLR+.