Abstract

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.
Keywords Knowledge representation; Knowledge organization; Artificial Intelligence; Paradigms

Note

Knowledge organization systems (KOS) are developed to represent knowledge in publications and in natural and societal environments and used for information discovery and retrieval.
By contrast, knowledge representation (KR) in artificial intelligence (AI) applications produces a set of statements that express facts, relations, and conditions in formal languages or schemes upon which reasoning can be performed to determine actions or reach conclusions. The reasoning component is perhaps the most striking difference in KR between traditional knowledge organization and artificial intelligence (AI).
Over the course of 30 years of KR research, the symbolic and connectionist paradigms have been prevalent in the AI field.
In general, KO works at the conceptual level and uses language to describe concepts with phrases or terms, while KR focuses on formalizing the expressions in natural language as well as other types of data to enable reasoning as in human intelligence.
This seemingly wide gap between the KO and KR paradigms is being bridged by ontologies that have become popular since Berners-Lee et al. (2001) proposed the concept of semantic web. It turned out to be revolutionary for the traditional KO paradigm; it prompted the community to reexamine the KOS structures and explore ways for KOS to fully take advantage of technology advances.

While transforming existing KOS into structured data does have great value for open access and reuse of such data, it does not address the challenges in acquiring new knowledge for knowledge organization systems, a well-known bottleneck problem for knowledge representation in AI research. Both KO and KR face the same knowledge acquisition challenges not only because of the complexity of knowledge expressions in texts, data, and multimedia resources, but also due to the fast-changing language and terminologies in modern society and science and technology advances. Whether a logic-based, semantic net, or production-rule-based paradigm is used to represent knowledge, three things must be available for automatic knowledge acquisition: knowledge nodes (k-nodes), relations between the nodes, and rules (which may be labeled differently in different disciplinary fields). To address the bottleneck problem in knowledge acquisition will need an orchestration of natural language processing, machine learning, and clustering and classification techniques to acquire (new) knowledge from texts through clustering and classification (Fisher, 1987).

In the personal knowledge management scenario, using hybrid method to structure knowledge is feasible, as AI system is far from automating the whole process. So how does the SOTA hybrid system work?