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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
To analyze complex situations we use everyday analogies that allow us to invest in an unknown domain knowledge we have acquired in a known field. In this work the author proposes a modeling and analysis method that uses the analogy of the ecosystem to embrace the complexity of an area of knowledge. After a history of the ecosystem concept and these derivatives (nature, ecology, environment ) from antiquity to the present, the analysis method based on the modeling of socio-semantic ontologies is presented, followed by practical examples of this approach in the areas of software development, digital humanities, Big Data, and more generally in the area of complex analysis.
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Seitenzahl: 307
Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Introduction
1 Use of the Ecosystem Concept on the Web
1.1. For marketing
1.2. For personal data
1.3. For services and applications
1.4. For dynamic interactivity
1.5. For pictorial analogies
1.6. For the information and communication sciences
2 Ecosystem Modeling: A Generic Method of Analysis
2.1. Hypertextual gardening fertilized by the chaos of John Cage
2.2. An entrepreneurial experience
2.3. The maturation of a research project
3 Fundamental Principles for Modeling an Existence
3.1. Key concepts for thinking about knowledge ecosystems
3.2. Spinozist principles for an ethical ontology
3.3. Semantic knowledge management
4 Graphical Specifications for Modeling Existences
4.1. Principles of graphical modeling
4.2. Semantic maps
4.3. Graphical modeling rules
5 Web Platform Specifications for Knowledge Ecosystems
5.1. The generic management of resources
5.2. Principles for developing a Web ecosystem platform
Conclusion
C.1. Experiments: digital humanities and e-Education
C.2. Theoretical fields to whet the appetite
C.3. Scientific practices between calculable facts and sensible intuition
Appendix
A.1. Project planning the new platform
Bibliography
Index
End User License Agreement
List of Tables
3 Fundamental Principles for Modeling an Existence
Table 3.1.
Cardinalities of the ontological matrix
5 Web Platform Specifications for Knowledge Ecosystems
Table 5.1.
List of APIs external to the Knowledge Garden
Table 5.2.
List of scraping models
Table 5.3.
List of SPARQL entry points
Table 5.4.
The TRU of a university
Table 5.5.
The TRU departments of a university
Table 5.6.
The TRU department courses of a university
Table 5.7.
List of administrative levels of a French university
Table 5.8.
Path to an element of the hierarchy
Appendix
Table A.1.
General timeline of the project
Table A.2.
Project human resources
Table A.3.
Project budget
List of Illustrations
1 Use of the Ecosystem Concept on the Web
Figure 1.1.
The advertising ecosystem in Europe
Figure 1.2.
Ecosystem of a Web strategy
Figure 1.3.
A commercial vision of the digital ecosystem
. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 1.4.
Personal data ecosystem
Figure 1.5.
Ecosystem of Google services and applications
Figure 1.6.
Listen Wikipedia
Figure 1.7.
Ecosystem vision of management
Figure 1.8.
Ecosystem vision of blogging
Figure 1.9.
Dynamic representation of the tree
Figure 1.10.
Exquisite Forest
Figure 1.11(a).
Analogy of the ecosystem
Figure 1.11(b).
Analogy of the ecosystem
Figure 1.12.
Example of biological interface: Botanicus Interacticus
Figure 1.13.
Disciplinary use of the term “ecosystem”
. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 1.14.
Disciplinary use of the term “Hypertext”
. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
2 Ecosystem Modeling: A Generic Method of Analysis
Figure 2.1.
Planet analogy
Figure 2.2.
Garden analogy
Figure 2.3.
EvalActiSem: setting the visualization
Figure 2.4.
Archeosemantic layers. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 2.5.
Comparison of two users. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 2.6.
Cloud of bubbles from a folksonomy
Figure 2.7.
Venn diagram selection form
Figure 2.8.
Venn diagram: example of interaction
Figure 2.9.
Venn diagram for the selection of permutations
Figure 2.10.
Data filter interface
Figure 2.11(a).
Tweet Palettes made by students
Figure 2.11(b).
Tweet Palettes made by students
Figure 2.12.
Tweet Palette
3 Fundamental Principles for Modeling an Existence
Figure 3.1.
Spinozean principles of information – communication
Figure 3.2.
Spinozist information circuit
Figure 3.3.
Ontological principles of Philippe Descola
Figure 3.4.
IEML idea diagram. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
4 Graphical Specifications for Modeling Existences
Figure 4.1.
Organization of UML diagrams
Figure 4.2.
Gallica map tools
Figure 4.3.
Example of a Gallica fragment
Figure 4.4.
Dendrochronology
Figure 4.5.
Stratigraphy
Figure 4.6.
Tree of elements
Figure 4.7.
Proxemics of the word “proverb”
. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 4.8.
Venn logic. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 4.9.
Venn diagram with five entries
Figure 4.10.
Conceptual axis
Figure 4.11.
Bidirectional conceptual axis
Figure 4.12.
Complementary conceptual axis
Figure 4.13.
Conceptual radar
Figure 4.14
Spatio-temporal mapping of interpretations
Figure 4.15.
Modeling physical dimensions
Figure 4.16.
Modeling actors
Figure 4.17.
Modeling concepts
Figure 4.18.
Modeling relations
Figure 4.19.
Modeling processes
Figure 4.20.
Tools for exploring knowledge ecosystems
5 Web Platform Specifications for Knowledge Ecosystems
Figure 5.1.
Categorizing a page for a scraping algorithm
Figure 5.2.
Ontological triplet
Figure 5.3.
Simple model of an SQL database
Figure 5.4.
Multi-interpretation SQL model
Figure 5.5.
Generic database model
Figure 5.6.
Performance for importing Diigo feeds
Figure 5.7.
Historical evolution of the “ecosysteminfo” tag
Figure 5.8.
Historical evolution of the HTTP status
Figure 5.9.
Interface for selecting historical periods
Figure 5.10.
Nested bubble hierarchy
Figure 5.11.
Nested tree hierarchy
Figure 5.12.
Flow between a user and a web server
Figure 5.13.
Publishing management of translation flows
Figure 5.14.
Two implementations of the same graphical template
Figure 5.15.
Wheel of emotions. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 5.16.
HMI to harvest the emotions. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Figure 5.17.
JavaScript in GitHub
Figure 5.18.
Graphics modeling flow [CAR 99]
Figure 5.19.
Modeling from knowledge ecosystems. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Conclusion
Figure C.1.
Formula for the search of documentary references
Figure C.2.
Tantrix board game
Figure C.3.
A screenshot of ExplAgora
Appendix
Figure A.1.
The Gantt of the project. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
Guide
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e1
Digital Tools and Uses Set
coordinated byImad Saleh
Volume 6
Ecosystems Knowledge
Modeling and Analysis Method for Information and Communication
Samuel Szoniecky
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2018
The rights of Samuel Szoniecky to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2018935807
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-064-5
Introduction
The multiple modes of recognition and knowledge, by analogy, are inherent to any cognitive activity and to all thought.
Edgard Morin
It would be quite illusory to think that we could do without metaphor in order to qualify new objects.
Yves Jeanneret
This book presents the concept of the knowledge ecosystem from the point of view of the uses, theory and design of a platform for collective intelligence. The aim is to provide conceptual and computational tools with which we can analyze the complexity of information and communication through a generic modeling of info-communication existences.
The concept of the ecosystem is increasingly used to describe situations in which multiple actors have dynamic relationships. These include the ecosystems of digital economy or those of innovation. The Société française des sciences de l’information et de la communication or SFIC (the French Society of Information and Communication Sciences) is positioning itself within the field of digital humanities through the discourse of a “complex ecosystem”1. However, to date, there has been no book that presents this concept in a detailed and critical way. Similarly, there is no book which deals with the use of this concept within a generic method of analysis of info-communication processes.
Nevertheless, the issue is important; it affects all individuals concerned with a “knowledge society” [LYO 79, COL 15], and especially with the intellectual technologies that can manage it [SAD 15, VIE 15]. In this context, where digital data is becoming increasingly important, it is fundamental to understand and manage the ins and outs of these technologies, the information they produce and the communications that they generate. This is one of the main themes advocated in France by the Conseil national du numérique or CNN (National Digital Council) in its 2015 report on digital ambition:
“For individuals, this right to self-determination implies that they have access to this data, that they can read, modify, erase and choose what they want to do with it; but moreover, also decide which services have access to it” [CNN 15, p. 50].
Faced with these challenges and with the goal of helping people understand these issues, Joi Ito, the director of MIT Media Lab, offers principles to “live by”:
– “disobedience over compliance;
– pull over push;
– compasses over maps;
– learning over education;
– resilience over strength;
– risk over safety;
– practice over theory;
– diversity over ability;
– systems over objects;
– emergence over authority” [ITO 16]
2
.
Even if we cannot eliminate a form of provocation that is inherent to these propositions, we nevertheless wonder about the scope of such a discourse, for instance, when it is presented to a class of primary school children and their capacity to understand these principles, and especially to put them into practice. However, this is exactly our primary ambition: how can we make the mastery of knowledge accessible to as many people as possible? Can we accompany individuals in their discoveries of the world and provide them with the tools that will help them to perfect their learning? Can we design intellectual technologies to collectively increase the power of each person to take action?
To the questions of accompanying humans in their understanding of contemporary worlds through digital technologies, the control of non-biological existences which populate our ecosystems, in particular within the framework of the Internet of Things are added [SAL 17]. As the European Parliament concerns about civil law issues with regard to robotics show and the importance of designing a European agency to deal with these issues, it is important to know about these digital existences that are increasingly autonomous and ubiquitous [NOY 17]. Faced with this proliferation of “living” things, we need generic methods that help us understand what these digital existences are in order to control the consequences of their use, especially when these things are used in knowledge processes that trace the least learning activity. How can we evaluate the power of a digital object to take action r within an ecosystem? How can we control the information that these digital existences draw from our use of them?
The management of knowledge ecosystems by modeling the digital existences that populate them is also an important issue in digital humanities and more generally in the use of intellectual technologies [SZO 17a]. In this field, the multiplication of data and the algorithms that manipulate them sometimes obscures the reasoning and interpretations supported by the researchers. Can generic modeling be used to quantify and qualify the knowledge convened in the scientific discourse? Do these models provide an effective way to compare these discourses and use these comparisons to make recommendations?
In this work, we propose an analytical method of information and communication that uses the analogy of the ecosystem to embrace all the complexity of this field. After a presentation on the uses of the concept of ecosystem and its derivatives (nature, ecology, environment, etc.) on the Web (Chapter 1: Use of the Ecosystem Concept on the Web), we will detail our method of analysis. This method is based on the generic modeling of info-communication existences (Chapter 2: Ecosystem Modeling: A Generic Method of Analysis), which uses theoretical principles (Chapter 3: Fundamental Principles for Modeling an Existence) and graphs (Chapter 4: Graphical Specifications for Modeling Existences). Based on these principles, we present the technological frameworks that we use to develop a collective intelligence platform dedicated to knowledge management (Chapter 5: Web Platform Specifications for Knowledge Ecosystems). Finally, we will present the research tracks and experiments that still need to be carried out in order to further explore the field of knowledge ecosystems (Conclusion).
1
The web links for this book were verified on June 26, 2017.
http://www.sfsic.org/index.php/infos/lettres-sic-infos/archive/view/listid-1/mailid-336-flash-infos-manifeste-sic-et-ds-dh
2
We are using the translation proposed here:
http://www.internetactu.net/2017/02/15/vers-lintelligence-etendue
1Use of the Ecosystem Concept on the Web
The animal and the environment are two sides of the same process, the object and the subject of knowledge mutually defining one another.
Humberto Maturana
Without a doubt, ecology drives you mad; that is where one should start.
Bruno Latour
The concept of the ecosystem only appeared comparatively recently and has since been credited to the British ecologist Tansley, who first used the word in 1935. According to Dury, Tansley defines this concept as “a whole constructed by the relations that maintains the living species and the physical habitat that allows them to develop”. Moreover, he highlights the shifting nature of this arrangement: “It depends on exogenous or external factors such as temperature, sunlight, humidity, etc., and internal factors such as the population sizes of the living beings that occupy it. The ecosystem is constantly changing according to these factors” [DUR 99, p. 488].
However, long before this word appeared in the field of ecology, we find intellectual practices that hypothesize a system of relations between living populations. Above all, some of these make the link between the organization of living beings and that of knowledge. We think, for example, of the notion of a garden which throughout antiquity up until today has been used as an analogy to reflect upon the human condition in relation to knowledge [HAR 07], or alternatively, to the I Ching, a complex theory that transposed the vicissitude of natural elements so as to model archetypes based on human behavior and the contrivance of these transformations, just as the alchemists of the European Middle Ages did [JUN 88], preoccupations that continue to be prominent in the writings of Haeckel, one of the inventors of ecology that bases this new science on three closely related aspects:
1) “the study of nature as knowledge of the truth (
Das Währe
),
2) ethics as the search for good (
Das Gute
),
3) esthetics as the search for beauty (
Das Schöne
)” [DEB 16, section 24]
This very rapid historical development lays the groundwork for more in-depth research that should be conducted in order to understand the evolution of a thought that associates living-beings and knowledge in the same vision. This work goes beyond the scope of this book which will focus more on the recent usage of the concept of ecosystems in terms of the World Wide Web.
To understand the usage of the ecosystem concept, we began monitoring the Web in 2006 up until now and collected 521 documents which we categorized according to 501 keywords. In the following sections, we will analyze this observation through the themes that seem most relevant to us1.
1.1. For marketing
The first theme we will explore is the most common found online: it concerns the usage of the ecosystem concept in the field of marketing and business. In this context, the linking of a multitude of products or services around a market is represented in graphs that illustrate the concept of the business ecosystem [ASS 16]:
Figure 1.1.The advertising ecosystem in Europe
Keeping in the same field, this next example shows how the term ecosystem is used to illustrate the relationships between different actors and how these actors define strategies for the implementation of a marketing campaign:
Figure 1.2.Ecosystem of a Web strategy2
The final example that we present below highlights one of the limitations of using the ecosystem concept, in that the notion is used here to define a marketing process as well; however, this time, the graphic does not illustrate the complexity of an ecosystem but rather the linearity of a commercial discourse:
Figure 1.3.A commercial vision of the digital ecosystem3. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
In Figure 1.3, the ecosystem concept is used only to insist on a multiplication of the elements; however, all the complexity of the processes is blurred in favor of a single type of relation: the production of money.
1.2. For personal data
Another use of the ecosystem concept is the management of personal data and its construction within a space made up of technologies, networks, data and humans. The example below summarizes how an individual is at the origin of a universe of interactions through a “personal cloud”. As we can see, the ecosystem of personal data embraces a wide range of interconnected services where governance forms the basis and the main problem.In this illustration, we note that there is no connection that returns to the individual; this feedback loop is nevertheless a fundamental notion of an ecosystem (see section 3.1.3) and even more central to the notion of personal data management. Indeed, how do we give individuals the means to take control of their data without the possibility of reflective manipulation?
Figure 1.4.Personal data ecosystem4
The management of personal data and its impact on the construction of a digital identity is becoming all the more important in the current era of the Internet of Things and the quantified self. This is evidenced by the CNIL publication on “the new body as a connected object”, and more particularly the section dedicated to the “ecosystem and performance”5.
1.3. For services and applications
Beyond the business and marketing aspects, the “Web Giants” (Google, Apple, Facebook, Amazon, Microsoft, also known as GAFAM) develop ecosystems through the multiplication of services and applications.
In order to capture the attention of users, GAFAM deploys a multitude of services and applications whose operation is conditioned with respect to the technical and legal rules of each company. To use these resources, you must necessarily enter the ecosystem of these companies as shown by the popup windows that offer to connect you through your account to a particular company, which thus becomes your identity provider (see section 5.1.2.8).
Figure 1.5.Ecosystem of Google services and applications
In the case of Google, there are a hundred services that are available to users and especially developers who, by using them, will hybridize the Google ecosystem in other areas. Therefore, Google will multiply its ecosystem by giving developers the means to build their own niche markets (see section 5.1.2.3). This raises the question as to the accessibility of these ecosystems and their eventual transformation into “walled gardens”:
“From an immense open ecosystem, the Web of today is a succession of what Tim Berners-Lee calls ‘walled gardens’, founded on proprietary data and the alienation of their users by prohibiting any form of sharing with the outside. The challenge is no longer simply that of open data, but that of metacontrol, that is, the increased control over the migration of our essential data hosted on the servers of these companies, as a result of the trivialization of cloud computing: most of the documentary material that defines our relationship with information and knowledge is about to end up in the hands of a few commercial society” [ERT 11, p. 11].
1.4. For dynamic interactivity
Even if today the dynamic interactivity of a web page seems to be commonplace, it is one of the more important aspects that transforms the Web from a simple document into a living knowledge ecosystem. Since the advent of Web 2.0 and the publication of content that is accessible to all through the simple tools that are social networks, that is, content management tools (see section 5.1.2.9) or services and applications, the Web is teeming with knowledge that is constantly appearing, updating or disappearing. What are totally new in the life cycle of the Web document are real-time updates and the possibility of tracing successive updates. As a result of these two characteristics, we can follow the “pulsations” of the Web as if one is observing a living ecosystem.
For example, the “Listen Wikipedia” web application shows changes to Wikipedia in the form of bubbles that appear and produce a particular sound that is calculated automatically:
Figure 1.6.Listen Wikipedia
1.5. For pictorial analogies
In parallel with the conceptual usage of the ecosystem as a notion, discussed above, our observation revealed instances where this notion of using the analogy with ecosystems was used as a model to organize the graphic and thematic presentation of a site or an application.
The simplest usage is the creation of a domain name related to ecosystems, for example, through the notion of a garden, and to simply use this theme to design an editorial line. This is the case, for example, of a site like https://www.opengarden.com/, which sells an application, allowing the sharing of information between several devices. If we cannot argue that the linkage is actually connected to the ecosystem notion, the analogy is not pushed further than the name and a logo. We could multiply the examples of this type of site that make a very basic use of analogy. On the contrary, there are other sites that go a little further in their use of the ecosystem notion, especially those seeking to describe an organization of work. For example, on this website of a Web agency, we find Figure 1.7, which seeks to highlight the aspect of an ecosystem in its approach:
Figure 1.7.Ecosystem vision of management6
Figure 1.8.Ecosystem vision of blogging7
In the same type of use, we find another illustration (Figure 1.8) which explains how the development of a blog is a complex thing that requires different phases of work.
We note that these last two illustrations represent an analogy of the ecosystem given that they use a plant/tree as the core image that is linked to a landscaped context that clearly marks its anchorage in an ecosystem where branches are in contact with the sky and related to the roots that are in contact with the earth. This distinction is important because it makes it possible to not consider all the uses of the tree principle as analogies of the ecosystem. Indeed, even though the hierarchical menu found everywhere on computer screens is probably inspired by a tree structure, this by itself does not correspond to an ecosystem approach.
The analogy of ecosystems is used on the Web not only as fixed representations of concepts, but also in dynamic representations that will “grow” as the image is constructed or viewed:
Figure 1.9.Dynamic representation of the tree8
The example, Figure 1.10, this time shows how to grow an ecosystem forest by proposing that contributors grow trees through the creation of short graphic animations.
The examples we have just presented illustrate how, through data originating from the Web, it is possible to build an ecosystem-inspired representation. It is precisely this analogy that Tim Berners-Lee and Hans Rosling use in their presentation at the TED conference to explain the structure of Web ecosystems and how its future will require the statistical and dynamic modeling of the information environment.
Figure 1.10.Exquisite Forest9
Figure 1.11(a).Analogy of the ecosystem10
Figure 1.11(b).Analogy of the ecosystem
Regarding the relationship between knowledge ecosystems and biological ecosystems, we should note that beyond the representation of a plant on a screen, the future may lie in the development of tangible interfaces that directly use a real plant as an interface for manipulating information. Technologies that make plants interactive, such as those proposed at the SIGGRAPH ‘12 conference by the Disney research laboratory [POU 12] or the “EmotiPlant” interface [ANG 15], suggest that this type of interface is not science fiction, and may well appear soon.
Figure 1.12.Example of biological interface: Botanicus Interacticus
1.6. For the information and communication sciences
In scientific literature, there are many occurrences of the term ecosystem that are used to describe very different contexts. The impact of this notion of an ecosystem in the information and communication sciences (ICS) still needs to be analyzed in detail, but some clues show both an old interest and, for example, the notion of “hypertextual gardening” [BAL 96, p. 170], as recently evidenced by the EUTIC 2015 conference on digital ecosystems or even more recently as part of the call for papers for the conference on “Digital Ecologies” organized by l’École supérieure d’art et de design d’Orléans11. The status of the ecosystem concept in ICS is still minimal but has the propensity to grow12.
If we refer to ISIDORE13, the research platform for digital data in the humanities and social sciences, the term “ecosystem” yields nearly 30,000 results spanning 27 disciplines.
The diagram in Figure 1.13 shows a change in the number of references over time, indicating an increased importance of the notion of an ecosystem with the predominance found in geography that accounts for more than 26% of the total results, whereas ICS represents just under 4%. It should be noted that until the 2010s, the notion of an ecosystem in ICS represented barely 1% of the results (about 10 documents). After 2010, however, the percentage increased to reach more than 6% in 2015 (240 documents), which shows an increasingly significant use of the concept in this discipline.
Figure 1.13.Disciplinary use of the term “ecosystem”14. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
It is, of course, necessary to relativize this evolution in relation to the corpus of the platform which does not correspond completely with the evolution of scientific production in the information and communication sciences [FRO 13], but only to that which was digitized and accessible. As a comparison, here is another diagram that this time shows the disciplinary evolution of the term “hypertext”. The predominant discipline this time is the ICS, which represents nearly 22% of the final results for a smaller story that only began in 1983.
Figure 1.14.Disciplinary use of the term “Hypertext”15. For a color version of the figure, see www.iste.co.uk/szoniecky/ecosystems.zip
1
https://www.diigo.com/user/luckysemiosis?query=%23ecosysteminfo
2
Illustration:
https://www.mauricelargeron.com/referencement-socle-d-une-presence-internet/
3
Illustration:
http://www.bricebottegal.com/definition-histoire-web-analytics/
4
Illustration:
https://image.slidesharecdn.com/2015-ghc-kaliya-151021184700-lva1-app6891/95/ethical-market-models-in-the-personal-data-ecosystem-31-638.jpg?cb=1445453607
5
https://www.cnil.fr/sites/default/files/typo/document/CNIL_CAHIERS_IP2_WEB.pdf
6
http://darmano.typepad.com/logic_emotion/2007/06/agency-ecosyste.html
7
https://visual.ly/community/infographic/computers/blog-tree-new-growth
8
http://www.visualcomplexity.com/vc/project_details.cfm?id=37&index=37&domain=
,
http://www.riekoff.com/tree
9
http://www.exquisiteforest.com/
10
https://www.ted.com/talks/tim_berners_lee_on_the_next_web?language=en
,
https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen
11
Link to the conference archives:
https://hal.archives-ouvertes.fr/EUTIC2015
12
Link to the call for contribution:
http://calenda.org/409949
13
https://www.rechercheisidore.fr/apropos,
interview conducted on 11 June 2017
14
The dynamic and interactive version of the diagram can be found here:
http://gapai.univparis8.fr/jdc/public/graph/streamv?type=getHistoDiscipline&q=%C3%A9cosyst%C3%A8me
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The dynamic and interactive version of the diagram can be found here:
http://gapai.univparis8.fr/jdc/public/graph/streamv?type=getHistoDiscipline&q=hypertexte
2Ecosystem Modeling: A Generic Method of Analysis
It is argued that natural logic is full of shadows;
it demonstrates mathematical logic, lightness.
So who has reason? The one who seeks what separates them or the one who seeks what unites them?
J.B. Grize
Complexity saves logic like a hygienic thought and makes transgressions like a thought mutilator.
Edgar Morin1
We live in an increasingly complex world whose analysis is also becoming increasingly complex. It can only be envisaged through collectively organizing our intelligence. There are examples of an epistemological framework whose purpose is to guide the researcher in their analysis. The one proposed by A. Coutant and J.-C. Domenget [COU 14] is particularly interesting because it is dedicated to scientific investigations of unstabilized phenomena whose fleeting nature recalls the context of an ecosystem. However, we will deviate from this approach. We will look more at the advantages found in the proposals of Mioara Mugur-Schächter [MUG 06] or Pierre Lévy [LÉV 11] whose ambition is at a more generic level. Our purpose is less about the expression of ideas than the algorithmic processing of their symbolic expressions.
In other words, what interests us here concerns less the description of a semantic terroir, that is, a sign system whose meaning evolves according to the place and the temporal fluctuations, but rather the modeling of the seeds that will grow in this terroir.
To carry out this project, it is necessary to take into account the entire design chain of a computer platform from its interactive design via ergonomic human–machine interfaces to visualization and data processing algorithms and ultimately the database. As noted in [JAT 16], it is a complete ecosystem that must be designed:
“Via the creation of digital databases in the Humanities and the Social Sciences (HSS), this is a whole research habitat that is taking place whose ecology deserves more consideration.” [JAT 16, section 9]2
The objective of this chapter is to present this ecosystem through the different stages of a collective intelligence platform project for the generic analysis of knowledge. The design work that we present here is spread over 20 years, which we propose to trace here, in order to show how this project has evolved in terms of the different phases of research action.
2.1. Hypertextual gardening fertilized by the chaos of John Cage
When I was working in art history on the influence of the American artist John Cage, I quickly realized that I needed a tool to manage the relationships between artists, works and the opinions about them at different times and in different latitudes. To answer this need, I learned about databases, and more specifically modeling aspects, in order to find the right model to manage these networks of influences. At the same time, inspired by the chaotic processes of John Cage and the first reference works on hypertexts [BAL 96], I became interested in the generative dimensions of computing and the possibilities that it offered. Hypertextual gardening [BAL 96, p. 70] has proved to be a very challenging concept to meet information modeling needs as well as a design model for human–machine interfaces (HMIs) required for information retrieval, collation and evaluation.
From these ideas, the design for a collective intelligence platform emerged, and the result of several experiments that would be too lengthy to detail here. It is sufficient to say that these experiments consisted of:
– developing an automatic generator of philosophical text from the random search of a library;
– designing a multi-agent system to evaluate and design an adaptive hypertext;
– creating a knowledge universe through recursive algorithm modeling in 3D fullerenes
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in the form of galaxies and planets;
– equipping a car with a multimedia system for the spatio-temporal exploration of the cultures of a given territory.
2.2. An entrepreneurial experience
Before finally being called the Jardin des Connaissances or JDC (Garden of Knowledge), the collective intelligence platform that we designed was presented to Cap Digital (2006) as part of the first call for competitiveness cluster projects under the name of Jardin semantique (Semantic Garden). For this call for projects in knowledge engineering, we described the initial aspects of the platform. To show the timeliness of this project, as far as 10 years later, we will cite the core strategy behind this call for projects and the texts that were proposed in response to it.
2.2.1.Objectives
The objective of this project was to develop a cognitive 3D interface for knowledge management. It was designed to organize both human–machine and machine–machine interactions in the form of a massively multiplayer serious game. Its goal was to connect knowledge within spacetime by modeling a semantic universe that interacted with other semantic worlds.
2.2.2.Principle of the game
Just like The Glass Bead Game described by Hermann Hesse [HES 02], the “cognitive garden” aims to stimulate knowledge by showing its organization and its complexity. However, where The Glass Bead Game is for the elite, educated specifically to play and understand the meaning of the game, the one we will propose is for the general public (7 to 107 years). Instead of presenting the information with an abstraction that is difficult to understand, the Cognitive Garden uses the analogy of the garden to make the management of information obvious by drawing inspiration from gardening practices.
We propose setting up an ecological simulation system where the player aims to garden information. Specifically, they must plant seeds, cut and graft shoots, fertilize the roots, make the soil, share resources and choose information flows that continuously feed the growth of plants. The branches represent the texts, the images are the leaves, the sounds are the fruits and the videos are the flowers. The roots will illustrate the conceptual organization of knowledge.
The goal of gardening is to create semantic maps (see section 4.2) using simple graphic gestures: cut, paste, move, deform, color and name. The player will have thus organized a network of interrogations based on a treelike structure, and by using the Venn diagrams (schematic representation of sets with intersecting curves, see section 4.3.1). From this network, through a continuous exchange process, complex queries are generated and confronted with information flows (Google, Wikipedia, RSS feeds, other cognitive gardens, etc.). We thus obtain a representation of the relationship between a particular problem and a data source which allows us to automatically deduce what must be retained, ignored, proposed, etc.
