Knowledge Organization and Representation under the AI Lens

• Published in: Journal of data and information science (Warsaw, Poland) Vol. 5; no. 1; pp. 3 - 17
• Main Author: Qin, Jian
• Format: Journal Article
• Language: English
• Published: Antwerp Sciendo 01.02.2020; De Gruyter Poland
• Subjects: Algorithms; Archives & records; Artificial Intelligence; Data models; Dewey Decimal Classification; Documents; Indexing services; Knowledge organization; Knowledge representation; Librarians; Library and information science; Library collections; Library of Congress Subject Headings; Linked Data; Machine learning; Metadata; Ontology; Paradigms; Science; Semantic web; Semantics
• ISSN: 2543-683X; 2096-157X; 2543-683X
• DOI: 10.2478/jdis-2020-0002

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.The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.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.This is only a preliminary research with a case study as proof of concept.The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.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.