Title Details

Samuel Szoniecky, Laboratoire Paragraphe, Université Paris 8, France.

Chapter 1. Ecosystems of Collective Intelligence in the Service of Digital Archives 1
Samuel SZONIECKY

1.1. Digital archives 1

1.2. Collective intelligence 3

1.3. Knowledge ecosystems 5

1.4. Examples of ecosystems of knowledge  7

1.4.1. Modeling digital archive interpretation 7

1.4.2. Editing archives via the semantic web 10

1.4.3. A semantic platform for analyzing audiovisual corpuses 12

1.4.4. Digital libraries and crowdsourcing: a state-of-the-art 14

1.4.5. Conservation and promotion of cultural heritage 16

1.4.6. Modeling knowledge for innovation  18

1.5. Solutions  20

1.6. Bibliography 21

Chapter 2. Tools for Modeling Digital Archive Interpretation 23
Muriel LOUÂPRE and Samuel SZONIECKY

2.1. What archives are we speaking of? Definition, issues and collective intelligence methods  25

2.1.1. Database archives, evolution of a concept and its functions 25

2.1.2. The exploitation of digital archives in the humanities 27

2.1.3. The specific case of visualization tools 32

2.2. Digital archive visualization tools: lessons from the Biolographes experiment 34

2.2.1. Tools for testing  37

2.2.2. Tools for visualizing networks: DBpedia, Palladio 38

2.2.3. Multi-purpose tools (Keshif, Table)  40

2.3. Prototype for influence network modeling 44

2.3.1. Categorization of relationships 45

2.3.2. Assisted influence network entry 47

2.4. Limits and perspectives 50

2.4.1. Epistemological conflicts  51

2.4.2. The digital “black box”? 55

2.4.3. From individual expertise to group intelligence 56

2.5. Conclusion 57

2.6. Bibliography 58

Chapter 3. From the Digital Archive to the Resource Enriched Via Semantic Web: Process of Editing a Cultural Heritage 61
Lénaïk LEYOUDEC

3.1. Influencing the intelligibility of a heritage document 61

3.2. Mobilizing differential semantics 62

3.3. Applying an interpretive process to the archive 63

3.4. Assessment of the semiotic study 67

3.5. Popularizing the data web in the editorialization approach  70

3.6. Archive editorialization in the Famille™ architext 73

3.7. Assessment of the archive’s recontextualization 79

3.8. Bibliography 81

Chapter 4. Studio Campus AAR: A Semantic Platform for Analyzing and Publishing Audiovisual Corpuses 85
Abdelkrim BELOUED, Peter STOCKINGER and Steffen LALANDE

4.1. Introduction 85

4.2. Context and issues 86

4.2.1. Archiving and appropriation of audiovisual data 89

4.2.2. General presentation of the Campus AAR environment 94

4.3. Editing knowledge graphs – the Studio Campus AAR example 96

4.3.1. Context 97

4.3.2. Representations of OWL2 restrictions 99

4.3.3. Resolution of OWL2 restrictions 101

4.3.4. Relaxing constraints 102

4.3.5. Classification of individuals 104

4.3.6. Opening and interoperability with the web of data 106

4.3.7. Graphical interfaces 107

4.4. Application to media analysis  108

4.4.1. Model of audiovisual description 109

4.4.2. Reference works and description models 110

4.4.3. Description pattern 111

4.4.4. The management of contexts 112

4.4.5. Suggestion of properties 113

4.4.6. Suggestion of property values 114

4.4.7. Opening on the web of data 115

4.5. Application to the management of individuals 116

4.5.1. Multi-ontology description 116

4.5.2. Faceted browsing 117

4.5.3. An individual’s range 117

4.6. Application to information searches 118

4.6.1. Semantic searches 118

4.6.2. Transformation of SPARQL query graphs 120

4.6.3. Transformation of OWL2 axioms into SPARQL 120

4.6.4. Interface 121

4.7. Application to corpus management 122

4.8. Application to author publication 123

4.8.1. Publication ontologies 125

4.8.2. Transformation engine 128

4.8.3. Final product 129

4.8.4. Opening on the web of data 129

4.8.5. Graphical Interface.130

4.9. Conclusion 131

4.10. Bibliography 132

Chapter 5. Digital Libraries and Crowdsourcing: A Review 135
Mathieu ANDRO and Imad SALEH

5.1. The concept of crowdsourcing in libraries 136

5.1.1. Definition of crowdsourcing 136

5.1.2. Historic origins of crowdsourcing 137

5.1.3. Conceptual origins of crowdsourcing 140

5.1.4. Critiques of crowdsourcing. Towards the uberization of libraries? 140

5.2. Taxonomy and panorama of crowdsourcing in libraries 141

5.2.1. Explicit crowdsourcing 143

5.2.2. Gamification and implicit crowdsourcing 145

5.2.3. Crowdfunding 148

5.3. Analyses of crowdsourcing in libraries from an information and communication perspective 150

5.3.1. Why do libraries have recourse to crowdsourcing and what are the necessary conditions? 150

5.3.2. Why do Internet users contribute? Taxonomy of Internet users’ motivations  153

5.3.3. From symbolic recompense to concrete remuneration  154

5.3.4. Communication for recruiting contributors 155

5.3.5. Community management for keeping contributors 155

5.3.6. The quality and reintegration of produced data 156

5.3.7. The evaluation of crowdsourcing projects 157

5.4. Conclusions on collective intelligence and the wisdom of crowds 158

5.5. Bibliography 159

Chapter 6. Conservation and Promotion of Cultural Heritage in the Context of the Semantic Web 163
Ashraf AMAD and Nasreddine BOUHAÏ

6.1. Introduction 163

6.2. The knowledge resources and models relative to cultural heritage 164

6.2.1. Metadata norms  164

6.2.2. Controlled vocabularies 171

6.2.3. Lexical databases 172

6.2.4. Ontologies 172

6.3. Difficulties and possible solutions 174

6.3.1. Data acquisition  175

6.3.2. Information modeling 185

6.3.3. Use 195

6.3.4. Interoperability  197

6.4. Conclusion 201

6.5. Bibliography 202

Chapter 7. On Knowledge Organization and Management for Innovation: Modeling with the Strategic Observation Approach in Material Science 207
Sahbi SIDHOM and Philippe LAMBERT

7.1. General introduction 207

7.2. Research context: KM and innovation process 210

7.2.1. Jean Lamour Institute 210

7.2.2. Technology and Knowledge Transfer Office (or CC-VIT) 211

7.3. Methodological approach 212

7.3.1. Observation and accumulation of knowledge for innovation 212

7.3.2. Strategic observation and extraction of knowledge: towards an ontological approach 215

7.3.3. Creation of a class hierarchy (of knowledge)  224

7.4. Conceptual modeling for innovation: technological transfer 225

7.4.1. Implementations 226

7.4.2. Corpus specificities 227

7.4.3. NLP engineering applied to the corpus 228

7.4.4. “Polyfunctionalities” favoring strategic observation 232

7.5. Conclusion: principal results and recommendations 233

7.6. Bibliography 235

List of Authors 239

Index 241