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NEOntometrics - A Public Endpoint for Calculating Ontology Metrics

Authors Achim Reiz , Kurt Sandkuhl

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TGDK.2.2.2.pdf
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Achim Reiz
  • Rostock University, Germany
Kurt Sandkuhl
  • Rostock University, Germany

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Achim Reiz and Kurt Sandkuhl. NEOntometrics - A Public Endpoint for Calculating Ontology Metrics. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 2:1-2:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/TGDK.2.2.2

Abstract

Ontologies are the cornerstone of the semantic web and knowledge graphs. They are available from various sources, come in many shapes and sizes, and differ widely in attributes like expressivity, degree of interconnection, or the number of individuals. As sharing knowledge and meaning across human and computational actors emphasizes the reuse of existing ontologies, how can we select the ontology that best fits the individual use case? How do we compare two ontologies or assess their different versions? Automatically calculated ontology metrics offer a starting point for an objective assessment. In the past years, a multitude of metrics have been proposed. However, metric implementations and validations for real-world data are scarce. For most of these proposed metrics, no software for their calculation is available (anymore). This work aims at solving this implementation gap. We present the emerging resource NEOntometrics, an open-source, flexible metric endpoint that offers (1.) an explorative help page that assists in understanding and selecting ontology metrics, (2.) a public metric calculation service that allows assessing ontologies from online resources, including GIT-based repositories for calculating evolutional data, with (3.) a scalable and adaptable architecture. In this paper, we first evaluate the state of the art, then show the software and its underlying architecture, followed by an evaluation. NEOntometrics is today the most extensive software for calculating ontology metrics.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Ontology engineering
  • Information systems → Web Ontology Language (OWL)
  • General and reference → Metrics
  • General and reference → Evaluation
Keywords
  • Ontology Metrics
  • Ontology Quality
  • Knowledge Graph Semantic Web
  • OWL
  • RDF

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