KOS in AI and AI in KOS
Submission Deadline: 31 May 2025
Guest Editors

Taxonomy Strategies, Washington, DC, USA
Interests: taxonomy development; metadata; semantic technologies; content modeling; enterprise content management; document management; search engines; intranets; information retrieval; business intelligence; text mining; knowledge management

Kent State Univeristy, Kent, OH, USA
Interests: knowledge organization and representation; metadata architectures and applications; linked data; thesaurus, taxonomy, ontology, and other knowledge organization systems (KOS); digital humanities
Special Issue Information
Dear Colleagues,
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). 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 KO: Knowledge Organization (ISSN 0943-7444) we are particularly interested in how knowledge organization systems (KOS) are being used or can be used to make automation intelligent. For example, one problem with LLMs is “hallucinations” where the application generates a response to a prompt that is “correct” but not true. How can KOS be integrated with LLMs to guide their responses so that they do not produce “hallucinations”? Potential topics include, but are not limited to:
- KOS use to make automation intelligent in different disciplines such as medicine, cultural heritage, environment, sustainability, etc.
- Applications of machine learning, natural language processing, language models, and related technologies to knowledge organization including both the use of KOS in AI and the use of AI for KOS.
We invite authors to submit a paper for consideration for this special issue. Please submit full papers through https://imr.propub.com/. Select the Section for the Special Issue: “KOS in AI and AI in KOS”. Author guidelines must be strictly followed: https://www.imrpress.com/journal/KO/instructions. Contact the guest editors Joseph Busch (jbusch@taxonomystrategies.com) and Marcia Zeng (mzeng@kent.edu) about whether a submission is within the scope of the special issue.
Prof. Joseph Busch and Prof. Marcia Zeng
Guest Editors
Keywords
- knowledge organization systems
- artificial intelligence
- machine learning
- natural language processing
- language models
Published Papers (3)
AI for KOS Discovery: Refining Search, Recommendation, and Hallucination Mitigation
Knowl. Organ. 2025, 52(4), 45889; https://doi.org/10.31083/KO45889
(This article belongs to the Special Issue KOS in AI and AI in KOS)
Knowledge-Based Ontology Model With a Case Study
Knowl. Organ. 2025, 52(4), 45165; https://doi.org/10.31083/KO45165
(This article belongs to the Special Issue KOS in AI and AI in KOS)
From Editorial Records to Structured Provenance Information: Documenting Warrant in Knowledge Organization Systems Using Large Language Models
Knowl. Organ. 2025, 52(3), 39046; https://doi.org/10.31083/KO39046
(This article belongs to the Special Issue KOS in AI and AI in KOS)
