Publications· 2026Q1
Design and Construction of an Ontology Model for Semantic Representation of Scientometric Indicators
- 0citations
- Q1SCImago
- 2026year
Short summary
A novel ontology, SciOnt, is developed to formally represent diverse scientometric indicators, enabling improved analysis, comparison, and development of these metrics.
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Key points
- A novel ontology, SciOnt, is proposed for the semantic representation of scientometric indicators.
- SciOnt addresses the complexity and diversity of indicators by providing a formal and structured knowledge representation.
- The ontology defines classes, relationships, and instances of scientometric indicators.
- The design methodology included indicator classification, competency questions, and construction using Protégé 5.5.
- The ontology was evaluated and its structure visualized.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper proposes a novel ontology, called SciOnt, for representing scientometric indicators. Scientometrics is the field that studies the quantitative aspects of science and scientific phenomena. Scientometric indicators are used to evaluate scientific publications and research. However, many indicators and their diversity make them challenging to understand and use. SciOnt addresses this challenge by providing a formal and structured representation of scientometric indicator knowledge. It defines classes, relationships, and instances of these indicators. The ontology can be used for various purposes, including improving the analysis and comparison of scientometric indicators, facilitating the development of new and more efficient indicators, and enhancing the understanding and criticism of existing indicators. The paper details the methodology used to design SciOnt, including identifying and classifying scientometric indicators, determining competency questions, and constructing the ontology with Protégé 5.5 software. Finally, the paper evaluates the ontology and visualizes the SciOnt ontology structure using various plugins.
The authors' abstract, as published at the source. Publications, 2026 · DOI ↗
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Statistics, Probability and UncertaintyDecision Sciences