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Table of Contents
Foreword; Preface; Table of Content; List of Figures; List of Tables; List of Abbreviations; PART I
Introduction, Economic Relevance, and Research Design ; 1 Introduction; 1.1 Initial Problem Statement; 1.2 Economic Relevance; 1.3 Organization of this Thesis; 1.4 Published Work; 1.4.1 Book Chapters; 1.4.2 Papers in Conference Proceedings; 1.4.3 Other Publications; 2 Research Design; 2.1 Semantic Technologies and Ontologies; 2.2 Research Goal; 2.3 Research Questions; 2.4 Research Methodology; 2.4.1 Design Science Research Methodology; 2.4.2 Ontology Development Methodology
PART II
Foundations: Data Quality, Semantic Technologies, and the Semantic Web 3 Data Quality; 3.1 Data Quality Dimensions; 3.2 Quality Influencing Artifacts; 3.3 Data Quality Problem Types; 3.3.1 Quality Problems of Attribute Values; 3.3.2 Multi-Attribute Quality Problems; 3.3.3 Problems of Object Instances; 3.3.4 Quality Problems of Data Models; 3.3.5 Common Linguistic Problems; 3.4 Data Quality in the Data Lifecycle; 3.4.1 Data Acquisition Phase; 3.4.2 Data Usage Phase; 3.4.3 Data Retirement Phase; 3.4.4 Data Quality Management throughout the Data Lifecycle
3.5 Data Quality Management Activities3.5.1 Total Information Quality Management (TIQM); 3.5.2 Total Data Quality Management (TDQM); 3.5.3 Comparison of Methodologies; 3.6 Role of Data Requirements in DQM; 3.6.1 Generic Data Requirement Types; 3.6.2 Challenges Related to Requirements Satisfaction; 4 Semantic Technologies; 4.1 Characteristics of an Ontology; 4.2 Knowledge Representation in the Semantic Web; 4.2.1 Resources and Uniform Resource Identifiers (URIs); 4.2.2 Core RDF Syntax: Triples, Literal Triples, and RDF Links; 4.2.3 Constructing an Ontology with RDF, RDFS, and OWL
4.2.4 Language Profiles of OWL and OWL 24.3 SPARQL Query Language for RDF; 4.4 Reasoning and Inferencing; 4.5 Ontologies and Relational Databases; 5 Data Quality in the Semantic Web; 5.1 Data Sources of the Semantic Web; 5.2 Semantic Web-specific Quality Problems; 5.2.1 Document Content Problems; 5.2.2 Data Format Problems; 5.2.3 Problems of Data Definitions and Semantics; 5.2.4 Problems of Data Classification; 5.2.5 Problems of Hyperlinks; 5.3 Distinct Characteristics of Data Quality in the Semantic Web; PART III
Development and Evaluation of the Semantic Data Quality Management Framework
6 Specification of Initial Requirements6.1 Motivating Scenario; 6.2 Initial Requirements for SDQM; 6.2.1 Task Requirements; 6.2.2 Functional Requirements; 6.2.3 Conditional Requirements; 6.2.4 Research Requirements; 6.3 Summary of SDQM's Requirements ; 7 Architecture of the Semantic Data Quality Management Framework (SDQM); 7.1 Data Acquisition Layer; 7.1.1 Reusable Artifacts for the Data Acquisition Layer; 7.1.2 Data Acquisition for SDQM; 7.2 Data Storage Layer; 7.2.1 Reusable Artifacts for Data Storage in SDQM; 7.2.2 The Data Storage Layer of SDQM; 7.3 Data Quality Management Vocabulary
Introduction, Economic Relevance, and Research Design ; 1 Introduction; 1.1 Initial Problem Statement; 1.2 Economic Relevance; 1.3 Organization of this Thesis; 1.4 Published Work; 1.4.1 Book Chapters; 1.4.2 Papers in Conference Proceedings; 1.4.3 Other Publications; 2 Research Design; 2.1 Semantic Technologies and Ontologies; 2.2 Research Goal; 2.3 Research Questions; 2.4 Research Methodology; 2.4.1 Design Science Research Methodology; 2.4.2 Ontology Development Methodology
PART II
Foundations: Data Quality, Semantic Technologies, and the Semantic Web 3 Data Quality; 3.1 Data Quality Dimensions; 3.2 Quality Influencing Artifacts; 3.3 Data Quality Problem Types; 3.3.1 Quality Problems of Attribute Values; 3.3.2 Multi-Attribute Quality Problems; 3.3.3 Problems of Object Instances; 3.3.4 Quality Problems of Data Models; 3.3.5 Common Linguistic Problems; 3.4 Data Quality in the Data Lifecycle; 3.4.1 Data Acquisition Phase; 3.4.2 Data Usage Phase; 3.4.3 Data Retirement Phase; 3.4.4 Data Quality Management throughout the Data Lifecycle
3.5 Data Quality Management Activities3.5.1 Total Information Quality Management (TIQM); 3.5.2 Total Data Quality Management (TDQM); 3.5.3 Comparison of Methodologies; 3.6 Role of Data Requirements in DQM; 3.6.1 Generic Data Requirement Types; 3.6.2 Challenges Related to Requirements Satisfaction; 4 Semantic Technologies; 4.1 Characteristics of an Ontology; 4.2 Knowledge Representation in the Semantic Web; 4.2.1 Resources and Uniform Resource Identifiers (URIs); 4.2.2 Core RDF Syntax: Triples, Literal Triples, and RDF Links; 4.2.3 Constructing an Ontology with RDF, RDFS, and OWL
4.2.4 Language Profiles of OWL and OWL 24.3 SPARQL Query Language for RDF; 4.4 Reasoning and Inferencing; 4.5 Ontologies and Relational Databases; 5 Data Quality in the Semantic Web; 5.1 Data Sources of the Semantic Web; 5.2 Semantic Web-specific Quality Problems; 5.2.1 Document Content Problems; 5.2.2 Data Format Problems; 5.2.3 Problems of Data Definitions and Semantics; 5.2.4 Problems of Data Classification; 5.2.5 Problems of Hyperlinks; 5.3 Distinct Characteristics of Data Quality in the Semantic Web; PART III
Development and Evaluation of the Semantic Data Quality Management Framework
6 Specification of Initial Requirements6.1 Motivating Scenario; 6.2 Initial Requirements for SDQM; 6.2.1 Task Requirements; 6.2.2 Functional Requirements; 6.2.3 Conditional Requirements; 6.2.4 Research Requirements; 6.3 Summary of SDQM's Requirements ; 7 Architecture of the Semantic Data Quality Management Framework (SDQM); 7.1 Data Acquisition Layer; 7.1.1 Reusable Artifacts for the Data Acquisition Layer; 7.1.2 Data Acquisition for SDQM; 7.2 Data Storage Layer; 7.2.1 Reusable Artifacts for Data Storage in SDQM; 7.2.2 The Data Storage Layer of SDQM; 7.3 Data Quality Management Vocabulary