Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

doi:10.1016/0169-023X(95)00017-M

Data & Knowledge Engineering

Volume 16, Issue 3, September 1995, Pages 191-222

https://doi.org/10.1016/0169-023X(95)00017-M Get rights and content

Abstract

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.

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PaperDifferent roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

Abstract

A knowledge representation framework supporting knowledge modelling

Knowledge-intensive case-based reasoning and learning

A computational model of knowledge-intensive problem solving and learning

A knowledge-intensive approach to problem solving and sustained learning

A case-based answer to some problems of knowledge-based systems

Explanation-driven case-based reasoning

Case-based reasoning: Foundational issues, methodological variations, and system approaches

AI Communications

Machine learning and knowledge acquisition in a computational architecture for fault diagnosis in engineering systems

The origins of pragmatism

Exemplar-based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning

Rigor Mortis: a response to Nillson's “Logic and artificial intelligence”

Artificial Intelligence

Intelligent Assistant Systems

On Knowledge Base Management Systems

Connectionism and information processing abstractions

AI Mag.

Task-structure analysis for knowledge modeling

Commun. ACM

The frame of reference problem in the design of intelligent machines

Object-oriented Analysis

Communications of the ACM

Special issue on Information Filtering

A philosophical basis for knowledge acquisition

Logic and databases: A deductive approach

ACM Computing Surveys

Structured Systems Analysis: Tools and Techniques

Aspects of Scientific Explanation

Abductive Inference, Computation, Philosophy, Technology

Usable and reusable programming constructs

Knowledge Acquisition

Improving human decision making through case-based decision aiding

AI Mag.

Case-based Reasoning

Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project

Case-based planning for medical diagnosis

Making expert systems explicit

Induction and reasoning from cases

Perspectives in deductive databases

J. Logic Programming

A framework for representing knowledge

Paper
Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration