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Semantics-based Decision Support - An Introduction
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
In order to guide our actions and achieve desired goals, human beings need to connect pieces of information together. Similarly, machines need to climb the steps up the wisdom hierarchy (commonly called the DIKW pyramid). The DIKW pyramid shows the hierarchy from data to wisdom: data (raw facts), information (defining relationships), knowledge (explicit information), and wisdom (thinking and acting using knowledge) [Koltay 2020]. Some applications require a combination of different domain knowledge to solve problems so that an appropriate action or conclusion can be deduced. However, combining domain knowledge is a very complex task, and it requires too much memory and time if the information is not organized in a proper format. A large amount of data comes from different places, which generates heterogeneity problem. The heterogeneity of data produces variation in meaning or ambiguity in the interpretation of entities; as a result, it prevents information sharing between systems. Therefore, without identification of the semantic mappings between entities, we cannot communicate, interact, collaborate, or share information across applications or use different knowledge sources in one application. Various approaches have been proposed to achieve solutions to these problems, but achieving optimal performance remains an open challenge. We need to convert data into knowledge artifacts to achieve interoperability; the most critical success factor is efficient and effective knowledge sharing across applications, organizations, and decision-makers, which in turn requires KM techniques.
Artificial Intelligence in Education
Published in Frank M. Groom, Stephan S. Jones, Artificial Intelligence and Machine Learning for Business for Non-Engineers, 2019
One paradigm shift that is clear is that the educator’s role will move higher up the value hierarchy. One model of knowledge known as the DIKW Pyramid (data–information–knowledge–wisdom) seeks to provide a framework around this. The basis of knowledge is data (e.g., an observation) upon which information (e.g., a general pattern based on many observations) can be synthesized. Knowledge suggests a useful application of information, e.g., leveraging a general pattern to make a conditional recommendation, or a system of decision-making. Finally, wisdom represents the highest level of consideration and would perhaps make a critical evaluation of a decision-making system and identify areas and methods of future improvement. In the next decade and beyond, AI will become the most effective teacher for bottom half of the DIKW pyramid: data, information, and basic knowledge. Educators will focus on the top half of the DIKW pyramid: knowledge and wisdom. Students will look to new AI-powered tools for technical skills, but will look to educators for a rich, critical discussion of how these skills fit into life and practice.
Application of Process Approach to OSH Management
Published in Daniel Podgórski, New Opportunities and Challenges in Occupational Safety and Health Management, 2020
Małgorzata Pęciłło, Anna Skład
What is known as the DIKW pyramid (data – information – knowledge – wisdom) can help to clarify the role of indicators in management (Ackoff 1989; Rowley 2007). This concept assumes that wisdom (located at the top of the pyramid) is the ability to improve effectiveness, in turn knowledge makes it possible to transform the information one possesses into instructions, while the latter are created on the basis of available data (constituting the lowest level of the pyramid). The concept states that data, information and knowledge, although necessary to acquire the wisdom that enables effective management, do not guarantee it. Moving to a higher level of the pyramid requires adequate processing of resources at lower levels.
Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools
Published in International Journal of Human–Computer Interaction, 2023
Aykut Coşkun, Armağan Karahanoğlu
Data is at the bottom of the data information, knowledge, wisdom (DIKW) pyramid (Rowley, 2007). It is raw and abstract (Ackoff, 1989; Bellinger et al., 2004). In most cases, data refers to symbols that require representations to make them understandable and usable (Zeleny, 1987). The usefulness of data increases when it is processed and turned into information (i.e., answers to what, who, and when questions), knowledge (i.e., answers to how-to questions), and understanding (i.e., answers to why questions) (Ackoff, 1989; Bellinger et al., 2004; Zeleny, 1987). Transition of data to wisdom (i.e., the top layer of the pyramid) requires human input. For Rowley (2007), cognitive input turns data into wisdom while this input increases data's meaning and applicability. For others (Ackoff, 1989; Bellinger et al., 2004), even though computers can turn data into information, knowledge, and understanding, wisdom requires human values and soul, which machines will never possess.