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A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges (2409.04432v3)

Published 6 Sep 2024 in cs.DL, cs.AI, and cs.IR

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.

Summary

  • The paper offers a comprehensive survey of 45 Knowledge Organization Systems, revealing their diverse scope, structure, and interdisciplinary coverage.
  • The paper examines varied curation methods, from manual to semi-automated approaches, emphasizing the necessity for regular updates.
  • The paper highlights key challenges in integration and multilingual support, advocating for automated interlinking among KOSs to improve accessibility.

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

Introduction

The paper presented by Salatino et al. offers an extensive survey on Knowledge Organization Systems (KOSs) within the academic field, encompassing term lists, thesauri, taxonomies, and ontologies. These systems play a pivotal role in structuring, managing, and retrieving academic knowledge across various domains, enhancing the classification and accessibility of research materials. The paper scrutinizes 45 KOSs across several dimensions such as scope, structure, curation, usage, and interlinking with other KOSs, highlighting their strengths, limitations, and practical implications.

Scope of KOSs

The survey identifies 22 KOSs covering multiple academic fields and 23 specializing in single disciplines. Nonetheless, the breadth of topic coverage varies significantly across these systems. While some fields like Medicine and Computer Science benefit from multiple specialized KOSs, others (e.g., History, Political Science) are underserved with no specific dedicated KOS. The paper indicates that only five multi-field KOSs consistently cover a broad spectrum of academic disciplines, hinting at a disparity in representation that suggests the need for more comprehensive systems.

Structural Characteristics

The paper reveals substantial variability in the structural attributes of KOSs. Some KOSs are extensive, with a number of concepts exceeding 3 million, while others are more constrained. Depth varies notably, with some systems exhibiting a shallow structure and others demonstrating a high level of granularity. For instance, the Open Biological and Biomedical Ontology showcases exceptional depth and breadth. The type of KOS also influences its structure and usage, with traditional taxonomies (23 KOSs) being more prevalent than ontologies (18 KOSs). The paper underscores the importance of poly-hierarchical structures in accommodating the complexity of some scientific domains.

Curation and Maintenance

The curation of KOSs involves diverse methodologies, ranging from manually curated systems to automatic and semi-automatic generation approaches. A trend towards automated or semi-automated updates is emerging, reflecting advances in AI and NLP technologies. For instance, OpenAlex Topics implements a semi-automatic pipeline combining manually curated and automatically generated research topics. Despite this, many KOSs remain reliant on manual curation, underscoring the ongoing necessity for expert involvement in ensuring quality and relevance.

The paper also highlights varying frequencies of updates, with some KOSs being updated annually and others lagging behind. Continuous and frequent updates are crucial for maintaining the relevance of KOSs, particularly in rapidly evolving fields. Nevertheless, the paper identifies logistical and technical challenges in achieving regular updates, suggesting a need for improved methodologies and tools.

The integration of KOSs and their interlinking with external knowledge systems are pivotal for their utility in digital ecosystems. The survey identifies 18 KOSs providing links to external resources, such as Wikidata and DBpedia, facilitating a richer contextual understanding and interoperability. The use of RDF and other semantic web technologies enhances the ability to create a seamless knowledge network, though the adoption of standard formats remains inconsistent.

The paper suggests that future research should focus on refining methods for generating inter-KOS links, possibly leveraging advanced AI methods for automated and semi-automated integration. This could mitigate the current limitations born out of manual mapping processes, which are time-consuming and prone to inconsistencies.

Multilingual Support

Only a subset of KOSs offers support in multiple languages, an essential feature for global interoperability and inclusivity. For example, the Agrovoc Thesaurus supports multiple languages but exhibits uneven distribution across them, with comprehensive support mainly in a few prominent languages. The paper suggests that extending multilingual support across KOSs is a significant challenge that requires innovative solutions, possibly aided by LLMs for efficient and accurate translations.

Challenges and Future Directions

The analysis identifies several critical challenges in the field of KOSs:

  • Comprehensiveness and Granularity: Developing a single KOS that is both comprehensive and granular across all scientific fields.
  • Integration: Improving methodologies for interlinking different KOSs, using standard formats, and adopting tools tailored for this task.
  • Multilingual Coverage: Enhancing language support to cover more non-English-speaking regions effectively.
  • Disagreement Management: Addressing conflicts among domain experts during the development and integration of KOSs.
  • Quality Assessment: Developing robust mechanisms to evaluate structural and conceptual quality.
  • Handling Ambiguities: Implementing advanced techniques for managing polysemy and context-specific meanings of terms.
  • Automated Updates: Leveraging AI to frequently update KOSs, capturing the dynamic nature of academic research.

Conclusion

The paper by Salatino et al. provides a valuable and comprehensive survey of KOSs, shedding light on their current state, challenges, and future directions. It emphasizes the need for collaborative efforts among the Open Science, Digital Libraries, and AI communities to develop integrated, high-quality, and comprehensive KOSs. The insights and resources from this paper pave the way for future advancements, aiming to better structure and retrieve academic knowledge across disciplines effectively.

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