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Data, Information, Knowledge and Wisdom, in short (DIKW), have been used widely as natural language marking terms in various domains for the purposes of expressing understandings. However there are still no unified understandings over the meaning of the DIKW concepts. Data, Information, Knowledge and Wisdom as a whole concept of DIKW is also missing unified understandings of the relationships among them. Therefore there has been proposals and models of DIKW as “layered hierarchy”, “architecture”, “framework”, “network”, “thinking mode”, “style”, “pattern”, “style”, “theory”, “methodology”, “model”, “graph”, etc.
Current scientific researches and engineering practices, as long as they aim at solving complexity based on precision and correction of conceptual sharing, are advancing ahead relying more and more on the solidification of the conceptual foundation. For example, to approaching strong AI involving art and social science in the direction of explainable AI, using current machine learning practice will need firstly not labeling ambiguous conceptual objects but identifying the semantics of the concepts in an objective manner if possible.
Recently AI practices have already been approaching multiple modals learning targets in terms of voice, picture, text, etc, rapidly, in various domains not limited to neuroscience, biology, medicine, industrial 4.0, digital twins, security and privacy protection, design, etc. These approaches cover multiple modals labeling, understanding, searching, reasoning, modification and especially and most recently embedding technologies in the form of various DIKW integration Machine Learning extensions. A foreseeable AI landscape with explainable and interactive human interactions is becoming feasible based on DIKW premises.
From the cognitive perspective, we propose to use multiple-modals for DIKW concepts as that Data modal as entity records of observations or abstraction of individual information with specific purposes, Information modal as purpose oriented relationships among DIKW targets, Knowledge modal as information typed with hypothesis of completeness, and Wisdom modal as human value bundled information. Towards the unification landscape of science and social science pictured in “Consilience:The Unity of Knowledge” by Dr. Edward O. Wilson, as DIKW modals are increasingly recognized as an important approach to solving problems related to semantic understanding beyond various question and answering systems, we are feeling more than ever the need to start by investigating on the necessary of unification of DIKW concepts in the background of relationship defined everything of semantics (RDXS) towards solving essence oriented computation and reasoning activities.
There might also be the cases that there will be new mode of Strong AI explorations such as we will not need to reach explicit unified understandings of the core concepts such as DIKW in the form of objective semantics but partially in the form of subjective semantics, which will still need supports or justification from cognitive psychology, philosophy, etc.
Under this background, 2021 Future Data, Information and Knowledge Research and Applications Conference (DIKW-RA 2021) aims to bring together scientists, researchers, and industrial engineers to discuss and exchange experimental and theoretical results, novel designs, work-in-progress and case studies on theories, design mechanisms and extensions on Data, Information, Knowledge and Wisdom interactions in all areas, in all phases, empirical or theoretical solutions. We hope you will join us for the great gathering of outstanding science, engineering, and technology, and look forwards to seeing you in the beautiful tropical city sanya!
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