Open AccessKnowledge Organization and Representation under the AI Lens
Author(s) -
Jian Qin
Publication year - 2020
Publication title -
journal of data and information science/journal of data and information science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 8
eISSN - 2543-683X
pISSN - 2096-157X
DOI - 10.2478/jdis-2020-0002
Subject(s) - originality , representation (politics) , vocabulary , computer science , value (mathematics) , convergence (economics) , knowledge management , key (lock) , knowledge representation and reasoning , through the lens metering , knowledge organization , management science , data science , artificial intelligence , linguistics , sociology , engineering , lens (geology) , social science , machine learning , political science , philosophy , computer security , politics , law , economics , economic growth , qualitative research , petroleum engineering
Purpose 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. Methodology The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper. Findings 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. Research limitations This is only a preliminary research with a case study as proof of concept. Practical implications The 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.