Collective Intelligence and Digital Archives: Towards Knowledge Ecosystems

252 Pages·2017·14.47 MB·English

by  Samuel Szoniecky

Collective Intelligence and Digital Archives
Digital Tools and Uses Set
coordinated by
Imad Saleh
Volume 1
Collective Intelligence and
Digital Archives

Towards Knowledge Ecosystems

Edited by

Samuel Szoniecky
Nasreddine Bouhaï
First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
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Contents
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
vi Collective Intelligence and Digital Archives
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
Contents vii
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
viii Collective Intelligence and Digital Archives
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
Contents ix
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
1
Ecosystems of Collective Intelligence
in the Service of Digital Archives

1.1. Digital archives
The management of digital archives is crucial today and for years to
come. It is estimated that every 2 days, humanity produces as much digital
information as was produced during the two million years that preceded our
existence. In addition to this human production is the information that
machines continuously produce. With the cost of digital memory becoming
ever cheaper, most of this information is stored in vast databases. In 2025,
all of these “big data” will constitute nearly eight zettabytes (trillions of
gigabytes) [SAD 15]. In our age, there are very few human activities that do
not generate digital archives; each day we feed digital workflows even
outside our use of computers, telephones or other digital devices. It is
enough for us to turn on a light, run errands, take public transport or watch
television to produce digital traces that, for the most part, will never be
accessible to us, but which are compiled, indexed and calculated in server
farms and management centers.
The status of these digital archives is obviously not the same when
dealing with the tweet sent automatically by a cow, the digitization of a
course by Gilles Deleuze or the 3D modeling of the Citadelle Laferrière near
Cap-Haïtien. Even if these archives are ultimately composed of a set of
0s and 1s and are therefore formally comparable to one another, their

Chapter written by Samuel SZONIECKY.
Collective Intelligence and Digital Archives: Towards Knowledge Ecosystems,
First Edition. Edited by Samuel Szoniecky and Nasreddine Bouhaï.
© ISTE Ltd 2017. Published by ISTE Ltd and John Wiley & Sons, Inc.
2 Collective Intelligence and Digital Archives
importance is not equivalent and they particularly vary according to space,
time and actor contexts that are faced with this information. The tweet sent
by a digital device in relation to a cow’s activities1 is probably not important
for most of us, but for the milk producer who wants to follow his herd’s
movements to correlate the milk composition with the pastures grazed, it is
important to know that a certain pasture has an influence on the amount of
fat in the milk. Similarly, a certain passage in Gilles Deleuze’s courses
where he speaks of the importance as a fundamental criterion seems to some
people like an almost meaningless phrase while it takes on very great
importance for the researcher interested in the relationship between ethics
and ontology, but also for the reader of these lines who at this very moment
is thinking about this concept just by the fact that they are reading it:
“What does that mean, this category? The important. No, it is
agreed; that is aggravating, but it is not important. What is this
calculation? Isn’t it that? Isn’t it the category of the remarkable
or the important that would allow us to establish proportions
between the two intransigent meanings of the word proportion?
Which depends on and results from the intensive part of myself
and which rather refers to the extensive parts that I have2.”
These proportions between the inner-being and the outer-having are quite
easily transposed into the domain of digital archives. Due to their dynamic,
upgradeable and interactive characters, digital archives are ecosystems
where each element can be analyzed in terms of existence made up of
“intensive parts” and “extensive parts”. The example of the digitization of
the fort at Cap-Haïtien sheds light on the importance of digital archives that
illustrate this “intensive/extensive” double dimension that Deleuze
emphasizes to show the correlation between an exterior dimension connected
to having and the material, and an interior dimension connected to being and
the immaterial. In the case of this historic monument classified as a
UNESCO World Heritage Site, digital archiving is the chance to develop
both a material and immaterial heritage in one of the poorest countries in the
world. The creation of an international research program focusing on the
issues of augmented realities, the teaching and education of students on these
issues, and the mobilization of artists for the innovative use of these
technologies are three examples of immaterial heritage development. At the

1 http://criticalmedia.uwaterloo.ca/teattweet/
2 http://www2.univ-paris8.fr/deleuze/article.php3?id_article=24