ViewpointS: capturing formal data and informal contributions into an adaptive knowledge graph

Brain Function Assessment in Learning

2020

Self Cite

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 ViewpointSthat 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.

Section: The Concept Of Serendipitymentioning

confidence: 99%

Section: Viewpoints: Emotions and Collective Wisdom For Serendipitymentioning

confidence: 99%

mentioning

confidence: 99%

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Serendipitous Learning Fostered by Brain State Assessment and Collective Wisdom

Brain Function Assessment in Learning

2020

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“…This section first briefly recalls the ViewpointS framework and formalism for building collective knowledge in the metaphor of the brain -a detailed description can be found in [20,21] -and then illustrates them through an imaginary case.…”

Section: The Viewpoints Approach Discussed and Exemplifiedmentioning

confidence: 99%

ViewpointS: A Collective Brain

Brain Function Assessment in Learning

2020

Self Cite

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.

“…This section first briefly recalls the ViewpointS framework and formalism for building collective knowledge in the metaphor of the brain -a detailed description can be found in [18], [19] -and then illustrates them through an imaginary case.…”

Section: The Viewpoints Approach Discussed and Exemplifiedmentioning

confidence: 99%

ViewpointS: Towards a Collective Brain

Computational Collective Intelligence

2018

Self Cite

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 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.