Introducing an Evaluation Method for Taxonomies

Kaplan, Angelika; Kühn, Thomas; Hahner, Sebastian; Benkler, Niko; Keim, Jan; Fuchß, Dominik; Corallo, Sophia; Heinrich, Robert

doi:10.1145/3530019.3535305

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Specifically, in the context of Wikipedia, Albuquerque (2017) presents an information architecture framework for the development and management of controlled vocabularies in the context of programming vocabulary projects. Kaplan et al . (2022) introduce an evaluation method for taxonomies, including structural quality criteria such as generality, appropriateness-attainment and orthogonality, and provide generalized metrics for quantifying generality and appropriateness.…”

Section: Methodsmentioning

confidence: 99%

“…into KOS. These foundational works underpin our proposed evaluation criteria for Wikipedia and Wikidata.Specifically, in the context of Wikipedia, Albuquerque (2017) presents an information architecture framework for the development and management of controlled vocabularies in context of programming vocabulary projects Kaplan et al (2022). introduce an evaluation method for taxonomies, including structural quality criteria such as generality, appropriateness-attainment and orthogonality, and provide generalized metrics for quantifying generality and appropriateness.In the domain of ontologies, daCosta et al (2022) provide an updated review of software architectures, including ontology usage for managing large volumes of data.Wilson et al …”

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confidence: 99%

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Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologies

Centelles,

Ferran-Ferrer

2024

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PurposeDevelop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.Design/methodology/approachThis study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.FindingsThis study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.Originality/valueThe assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologies

Centelles,

Ferran-Ferrer

2024

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“…We employ the method defined in [12] to evaluate the derived taxonomy. It defines a three-step approach to assessing the quality of the taxonomy based on structure suitability, applicability, and purpose.…”

Section: ) Properties For Taxonomy Evaluationmentioning

confidence: 99%

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

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Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

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