Towards Ordinal Data Science

Authors Gerd Stumme , Dominik Dürrschnabel , Tom Hanika

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Author Details

Gerd Stumme
  • Knowledge & Data Engineering Group, Research Center for Information System Design &, Department of Electrical Engineering and Computer Science, University of Kassel, Germany
Dominik Dürrschnabel
  • Knowledge & Data Engineering Group, Research Center for Information System Design &, Department of Electrical Engineering and Computer Science, University of Kassel, Germany
Tom Hanika
  • Institute of Computer Science, University of Hildesheim, Germany
  • Berlin School of Library and Information Science, Humboldt-Universität zu Berlin, Germany

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Gerd Stumme, Dominik Dürrschnabel, and Tom Hanika. Towards Ordinal Data Science. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 6:1-6:39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Ontology engineering
  • Computing methodologies → Nonmonotonic, default reasoning and belief revision
  • Computing methodologies → Semantic networks
  • Computing methodologies → Algebraic algorithms
  • Computing methodologies → Boolean algebra algorithms
  • Computing methodologies → Unsupervised learning
  • Computing methodologies → Inductive logic learning
  • Computing methodologies → Rule learning
  • Order relation
  • data science
  • relational theory of measurement
  • metric learning
  • general algebra
  • lattices
  • factorization
  • approximations and heuristics
  • factor analysis
  • visualization
  • browsing
  • explainability


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