Collective learning has been advocated to be at the source for innovation, particularly as serendipity seems historically to have been the driving force not only behind innovation, but also behind scientific discovery and artistic creation. Informal learning is well known to represent the most significant learning effects in humans, far better than its complement: formal learning with predefined objectives. We have designed an approach—ViewpointS—based on a digital medium—the ViewpointS Web Application—that enables and enhances the processes for sharing knowledge within a group and is equipped with metrics aimed at assessing collective and informal learning. In this article, we introduce by giving a brief state of the art about collective and informal learning, then outline our approach and medium, and finally, present and exploit a real-life experiment aimed at evaluating the ViewpointS approach and metrics.

With the experiment that we outline in this paper, we have had the ambition to pave the way for addressing the problem of supporting, enhancing and measuring collective AND informal learning, in particular serendipity. We want to support a new type of free navigation on Web resources (Documents, Topics, Events and Agents – human and artificial -) that is driven by the learner’s current needs and the preferences of the community of trust chosen by the learner, not by external actors. The experiment exploits the ViewpointS Web Application (VWA) prototype, that restructures a private version of a subset of the Web according to personalized choices in order to determine distances/proximities among resources. The process allows to enable, empower and measure the influence of members of the community of trust of the learner, on the learner’s choices when navigating in search of THE resources corresponding to THE immediate need, goal, strategy, wish. In the following, we will outline: 1. the rationale of our efforts and 2. the user’s reactions during the phase of -formal and informal- learning the functions and use of the prototypical software environment VWA, i.e.: a proof of concept for VWA.

Understanding and forecasting brain functions is the major challenge of our times. The focus of this endeavor is understanding and forecasting learning events, such as the dynamic adaptation of beams connecting neuronal cards in Edelman’s Theory of Neuronal Group Selection (TNGS). We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with the TNGS we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual brain functions as well as of collective wisdom at different degrees of granularity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.

Serendipitous discovery, invention or artistic creation are among the most exciting and utmost relevant phenomena strongly related to human learning . At the moment, there are very few measurable criteria helping to understand and foster serendipity. In other papers [ 1 – 3 ] we have presented, discussed and exemplified a new paradigm/model/method/system/environment – called ViewpointS – that represents our efforts to overcome many current existing limitations in generic Information Systems or search engines (e.g.: Google) as well as in other social media (e.g.: recommender systems) offering information retrieval solutions based on the proximity of available resources. We also have also exposed how ViewpointS may facilitate serendipitous discovery in an unprecedented way. In this paper, we wish to further motivate this last conjecture by proposing to explore two main research directions that did not convey sufficient attention by previous researchers (in particular those active in recommender systems): 1. assessing brain states in order to understand and forecast serendipitous human learning events triggered by emotions; 2. enhancing collective wisdom , since Human-Computer Interactions do not occur today between a human and a single machine (or algorithm), but within a community of humans and machines that continuously update “knowledge” beyond the scene. Both directions (assessment of brain states, collective wisdom) are currently on separate ways; we propose to combine them within one unified approach called ViewpointS.

Collective intelligence is one major outcome of the digital revolution, but this outcome is hardly evaluated. By implementing a topological knowledge graph (KG) in the metaphor of a brain, the ViewpointS approach attempts to trace and assess the dynamics of collaborative knowledge building. Our approach relies on a bipartite graph of resources (agents, documents, topics) and time stamped "viewpoints" emitted by human or artificial agents. These viewpoints are typed (logical, mining, subjective). User agents feed the graph with resources and viewpoints and exploit maps where resources are linked by "synapses" aggregating the viewpoints. They reversely emit feedback viewpoints which tighten or loosen the synapses along the knowledge paths. Shared knowledge is continuously elicited against the individual "systems of values" along the agents' exploitation/feedback loops. This selection process implements a rudimentary form of collective intelligence, which we assess through innovative metrics. In this paper, we present the exploitation/feedback loops in detail. We expose the mechanism underlying the reinforcement along the knowledge paths and introduce a new measure called Multi Paths Proximity inspired from the parallel neural circuits in the brain. Then we present the Web prototype VWA implementing the ViewpointS approach and set a small experiment assessing collective knowledge building on top of the exploitation/feedback loops.

Tracing knowledge acquisition and linking learning events to interaction between peers is a major challenge of our times. We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with Edelman's Theory of Neuronal Group Selection (TNGS), we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual as well as collective learning at different degrees of granu-larity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.

Formal data is supported by means of specific languages from which the syntax and semantics have to be mastered, which represents an obstacle for collective intelligence. In contrast, informal knowledge relies on weak/ambiguous contributions e.g., I like. Reconciling the two forms of knowledge is a big challenge. We propose a brain-inspired knowledge representation approach called ViewpointS where formal data and informal contributions are merged into an adaptive knowledge graph which is then topologically, rather than logically, explored and assessed. We firstly illustrate within a mock-up simulation, where the hypothesis of knowledge emerging from preference dissemination is positively tested. Then we use a real-life web dataset (MovieLens) that mixes formal data about movies with user ratings. Our results show that ViewpointS is a relevant, generic and powerful innovative approach to capture and reconcile formal and informal knowledge and enable collective intelligence.

Reconciling the ecosystem of semantic Web data with the ecosystem of social Web participation has been a major issue for the Web Science community. To answer this need, we propose an innovative approach called ViewpointS where the knowledge is topologically, rather than logically, explored and assessed. Both social contributions and linked data are represented by triples agent-resource-resource called viewpoints . A viewpoint is the subjective declaration by an agent (human or artificial) of some semantic proximity between two resources. Knowledge resources and viewpoints form a bipartite graph called knowledge graph . Information retrieval is processed on demand by choosing a user's perspective i.e., rules for quantifying and aggregating viewpoints which yield a knowledge map . This map is equipped with a topology: the more viewpoints between two given resources, the shorter the distance ; moreover, the distances between resources evolve along time according to new viewpoints, in the metaphor of synapses' strengths. Our hypothesis is that these dynamics actualize an adaptive, actionable collective knowledge. We test our hypothesis with the MovieLens dataset by showing the ability of our formalism to unify the semantics issued from linked data e.g., movies' genres and the social Web e.g., users' ratings. Moreover, our results prove the relevance of the topological approach for assessing and comparing along the time the respective powers of 'genres' and 'ratings' for recommendation.

Territorial development aims to renew public action and find solutions to major societal challenges. Making use of the new information and communication technologies, territorial observatories are also cooperative mechanisms that favour collective learning processes. Their implementation is complicated and difficult because it involves building, at the same time, an organization, a project and an information system in a process of mutual validation. CoObs, a method for the collaborative design of territorial observatories, is based on the deployment of three models: the model of territorial dynamics, the model of action and the model of observation. The article introduces and presents this method. Based on an analysis of two experiences, in the French West Indies and the Bassin de Thau in southern France, the authors show that territorial observatories contribute to the knowledge society by permitting citizen knowledge to inform societal choices and by reintroducing policy debates in processes of deliberative democracy. In addition to taking into account the objectives of the actors, a territorial observatory requires a high-level of technical expertise and leadership, and a political or institutional backing that ensures that means are available for its functioning over the long term. The implementation of a territorial observatory takes time; it takes a few years for it to become fully operational. A factor and metric of its success is the efficiency of its technical system, as evaluated by the quality of its responses to the needs and demands of the actors.

Le développement territorial a pour ambition de renouveler l'action publique et de répondre aux grands enjeux sociétaux. Mobilisant les nouvelles technologies de l'information et de la communication, les observatoires territoriaux sont aussi des dispositifs de coopération favorisant des processus d'apprentissage collectif. Leur mise en œuvre est difficile car il s'agit de construire simultanément une organisation, un projet et un système d'information dans un processus de validation mutuelle. CoObs, méthode de conception collaborative d'observatoires territoriaux, est basée sur l'élaboration de trois modèles : le modèle des dynamiques territoriales, le modèle de l'action et le modèle de l'observation. L'article introduit et présente cette méthode. À partir de l'analyse de deux expériences, aux Antilles et dans le Bassin de Thau, les auteurs démontrent que les observatoires territoriaux contribuent à la société de la connaissance en permettant aux savoirs citoyens d'éclairer les choix sociétaux et en réintroduisant le débat politique dans des processus de démocratie délibérative. Au-delà de la prise en compte des objectifs des acteurs, la mise en œuvre d'un observatoire territorial demande du temps (le processus se déroule sur plusieurs années), une animation technique de qualité, et un portage politique ou institutionnel garantissant des moyens. Un autre facteur de succès est l'efficience du dispositif technique, évalué par la qualité des réponses aux besoins et demandes des acteurs.

This is a position paper describing the author’s views on a potential new research direction for assessing, constructing and exploiting brain-founded models of learning of individual as well as collective humans. The recent approach – called ViewpointS – aiming to unify the Semantic and the Social Web, data mining included, by means of a simple “subjective” primitive – the viewpoint - denoting proximity among elements of the world, seems to offer a promising context of innovative empirical research in modeling human learning less constrained with respect to the previous three other ones. Within this context, a few phenomena of serendipitous learning have been simulated, showing that the process of collective construction of knowledge during free navigation may offer interesting side effects of informal, serendipitous knowledge acquisition and learning. We envision therefore an extension of the modeling functions within ViewpointS by adding measures of the emotions and mental states as acquired during experimental sessions. These brain-related components may in a first phase allow to describe and classify models in order to understand the relations among knowledge structures and mental states. Subsequently, more predictive experiments may be envisaged. These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions. We are convinced that useful applications may range, for instance, from Tutoring, to Health, to consensus formation in Politics at very low investment costs as the experimental set up consists of minimal extensions of the Web.

We first briefly recall the ViewpointS knowledge representation formalism and discuss the genericity it enables in terms of semantic distance computation. ViewpointS enables representation and storage of individual viewpoints in a shared knowledge graph. Knowledge providers (i.e., agents) express their individual opinions by emitting viewpoints on the semantic similarity or proximity between resources of the knowledge graph which can either be agents, documents (i.e., knowledge supports) or concepts (i.e., descriptors). In this paper, we benchmark the ViewpointS approach against other classic semantic distances (graph based or information content based) on a WordNet experiment. Our goal is to demonstrate the value of keeping the subjectivity of the represented knowledge, while having a generic approach that can handle any kind of knowledge and compute similarity between any kinds of objects.

The Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several “viewpoints” are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set.

The Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several “viewpoints” are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set.