<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Purpose</h3><p>This paper compares the paradigmatic differences between knowledge organization (KO) in library and information science and knowledge representation (KR) in AI to show the convergence in KO and KR methods and applications.</p></sec><sec><h3>Methodology</h3><p>The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.</p></sec><sec><h3>Findings</h3><p>A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations, while KR is problem-solving oriented. Differences between KO and KR are discussed based on the goal, methods, and functions.</p></sec><sec><h3>Research limitations</h3><p>This is only a preliminary research with a case study as proof of concept.</p></sec><sec><h3>Practical implications</h3><p>The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.</p></sec><sec><h3>Originality/value:</h3><p>Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.</p></sec></abstract>

PurposeThis paper compares the paradigmatic differences between knowledge organization (KO) in library and information science and knowledge representation (KR) in AI to show the convergence in KO and KR methods and applications.MethodologyThe literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.FindingsA key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations, while KR is problem-solving oriented. Differences between KO and KR are discussed based on the goal, methods, and functions.Research limitationsThis is only a preliminary research with a case study as proof of concept.Practical implicationsThe paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.

Knowledge Organization and Representation under the AI Lens

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Knowledge Organization and Representation under the AI Lens"}

PurposeThis paper compares the paradigmatic differences between knowledge organization (KO) in library and information science and knowledge representation...

Knowledge Organization and Representation under the AI Lens

Article Category: Research Paper

Publié en ligne: 22 avr. 2020

Pages: 3 - 17

Reçu: 10 janv. 2020

Accepté: 25 mars 2020

DOI: https://doi.org/10.2478/jdis-2020-0002

Mots clés
Knowledge representation, Knowledge organization, Artificial Intelligence, Paradigms

© 2020 Jian Qin, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Knowledge Organization and Representation under the AI Lens

Article Category: Research Paper

Publié en ligne: 22 avr. 2020

Pages: 3 - 17

Reçu: 10 janv. 2020

Accepté: 25 mars 2020

DOI: https://doi.org/10.2478/jdis-2020-0002

Mots clésKnowledge representation, Knowledge organization, Artificial Intelligence, Paradigms

© 2020 Jian Qin, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Knowledge representation, Knowledge organization, Artificial Intelligence, Paradigms