, Marcia Lei Zeng 21 Taxonomy Strategies, Washington, DC 20015, USA
2 School of Information, Kent State University, Kent, OH 44240, USA
Knowledge Organization Systems/Services/Structures (KOS) such as glossaries, classification systems, thesauri, and ontologies model the underlying semantic structure of a domain. Embodied as Web-based services, they can facilitate resource discovery and retrieval. They act as semantic road maps and make possible a common orientation by indexers and future users (whether human or machine) (Tudhope and Koch, 2004; Zeng, 2008). Artificial intelligence (AI) is broadly defined as the use of automation to solve problems by reasoning autonomously. Today, the popular AI method is large language models (LLMs). But there are many other automation methods such as rules-based, machine learning, vectors, n-grams, clustering, filtering, NLP (natural language processing), NLG (natural language generation), etc. that can make automation intelligent. While there is a tendency to focus on one primary method, most AI applications use several methods. In this special issue of Knowledge Organization (ISSN 0943-7444), five articles have been collected that discuss how KOS are being used or can be used to make automation intelligent.
• Naoual Smaili and Adil Kabbaj (INSEA - L’Institut National de Statistique et d’Economie Appliquée, Morocco) presents a knowledge-based ontology model that provides not only the definition of a word but also some of its typical uses.
• Sophy Shu-Jiun Chen (Academia Sinica Center for Digital Cultures, Taiwan) investigates integrating Generative AI in the thesaurus development process, for example, to identify candidate synonyms and help generating scope notes, LLM prompts, and expert validation.
• Julaine Clunis and Eric Asare (Old Dominion University, USA) provide a bibliometric study of the AI methods that have been discussed in medical journal articles over the past 10 years and speculate on trends in adoption of AI in medical research and treatments based on this analysis.
• Ziyoung Park (Hansung University, South Korea) explores how integrating Knowledge Organization Systems with generative artificial intelligence can enhance retrieval and discovery processes by mitigating the generation of hallucinations.
• Yi-Yun Cheng (Rutgers University, USA) and Inkyung Choi (OCLC Research, USA) describe the results of experiments to use LLMs to extract structured provenance information (warrant) from the editorial records of KOS.
JB and MLZ made substantial contributions to the conception and design of the article. JB drafted the manuscript, and MLZ provided critical revisions for important intellectual content. Both authors approved the final version to be published and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved.
Not applicable.
This research received no external funding.
The authors declare no conflict of interest. JB is affiliated with Taxonomy Strategies, which had no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript; or the decision to publish the results. JB serves as one of the Guest Editors, and MLZ serves as a Guest Editor and Editorial Board member of this journal. JB and MLZ were not involved in the handling of this manuscript and had no access to information regarding its editorial process. The full responsibility for the editorial handling of this article was delegated to NT.
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