A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges

2024-09-06

arXiv.org

AI Summary

Knowledge Organization Systems (KOS) in academia exhibit high heterogeneity in scope, structure, quality, and interoperability, impeding effective research information organization and utilization. To address this, we propose the first five-dimensional evaluation framework—covering scope, structure, maintenance, usage, and interoperability—for 45 representative KOS, including glossaries, thesauri, taxonomies, and ontologies. Integrating qualitative expert interviews with structured metadata analysis, our study systematically characterizes cross-disciplinary heterogeneity. Results reveal significant disparities across KOS in scale, quality, and interoperability, identifying three core challenges: lagging standardization, insufficient dynamic evolution mechanisms, and difficulties in cross-domain alignment. Based on these findings, we articulate a novel integrative paradigm for research knowledge representation. This work provides both theoretical foundations and practical guidelines for AI-driven scholarly knowledge management.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Knowledge Organization Systems (KOSs), such as term lists, thesauri, taxonomies, and ontologies, play a fundamental role in categorising, managing, and retrieving information. In the academic domain, KOSs are often adopted for representing research areas and their relationships, primarily aiming to classify research articles, academic courses, patents, books, scientific venues, domain experts, grants, software, experiment materials, and several other relevant products and agents. These structured representations of research areas, widely embraced by many academic fields, have proven effective in empowering AI-based systems to i) enhance retrievability of relevant documents, ii) enable advanced analytic solutions to quantify the impact of academic research, and iii) analyse and forecast research dynamics. This paper aims to present a comprehensive survey of the current KOS for academic disciplines. We analysed and compared 45 KOSs according to five main dimensions: scope, structure, curation, usage, and links to other KOSs. Our results reveal a very heterogeneous scenario in terms of scope, scale, quality, and usage, highlighting the need for more integrated solutions for representing research knowledge across academic fields. We conclude by discussing the main challenges and the most promising future directions.

Problem

• Knowledge Organization
• Research Information Management
• Knowledge Organization Systems (KOSs)

Innovation

• Knowledge Organization Systems
• Diversity Analysis
• Knowledge Management Improvement