Ontology Quality Evaluation Methodology

Wilson, R. S. I.; Goonetillake, Jeevani S.; Ginige, Athula; Indika, W. A.

doi:10.1007/978-3-031-10522-7_35

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…(2022) provide an updated review of software architectures, including ontology usage for managing large volumes of data. Wilson et al . (2022) outline a methodology for evaluating ontology quality that considers intrinsic and extrinsic aspects.…”

Section: Methodsmentioning

confidence: 99%

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%

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

Centelles,

Ferran-Ferrer

2024

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“…The quality of the matching process greatly depends on the quality of the input ontologies to be matched. Existing approaches defined in the literature have addressed the quality evaluation of ontology from different dimensions, such as syntactic, semantic, pragmatic, social, structural, functional, content, schema, and usage [6], [7], [8], [9]. Each dimension has its own set of criteria, and each criterion has its own set of metrics to measure the characteristics of an ontology that can be represented formally [10].…”

Section: Introductionmentioning

confidence: 99%

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

et al. 2023

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The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an indepth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.

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Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction

Vargas-Solar

2024

Electronics

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The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from these texts requires a deep understanding of natural language nuances and accurately representing content while preserving essential information. This process enables effective knowledge extraction, inference, and discovery. This paper proposes a critical study of state-of-the-art contributions exploring the complexities and emerging trends in representing, querying, and analysing content extracted from textual data. This study’s hypothesis states that graph-based representations can be particularly effective when annotated with sophisticated querying and analytics techniques. This hypothesis is discussed through the lenses of contributions in linguistics, natural language processing, graph theory, databases, and artificial intelligence.

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Ontology Quality Evaluation Methodology

Cited by 3 publications

References 40 publications

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

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

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction

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