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Title:
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CO-OPERATIVE KNOWLEDGE DISCOVERY BASED ON MEME MEDIA, NATURAL LANGUAGE PROCESSING AND THEORY OF MIND MODELING AND INDUCTION |
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Author(s):
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Oksana Arnold, Sebastian Drefahl, Jun Fujima, Klaus P. Jantke |
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ISBN:
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978-989-8533-60-9 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2017 |
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Edition:
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Single |
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Keywords:
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Data Analysis, Data Visualization, Data Exploration, Open Data, Big Data, Assistant Systems, Intelligent System Assistance, Adaptive Behavior, Knowledge Di |
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Type:
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Full Paper |
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First Page:
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27 |
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Last Page:
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38 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In contemporary information and communication technologies, there is an urgent need of transforming tools into assistant systems. Humans do not need more digital tools that require learning how to wield them, but digital assistants guiding them to unforeseeably valuable results. This applies particularly when dealing with wicked problems which change over time when being tackled. Data analysis, visualization, and exploration is a characteristic domain of this type. The paper demonstrates the transformation of a tool for big data analysis into an intelligent adaptive assistant. The transformation is based on the exploitation of concepts, methods, and technologies from disciplines such as meme media, natural language processing, and theory of mind modeling and induction. A case study serves as proof of concept. A human user co-operating with a digital assistant system arrives at surprising novel insights. |
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