Analysing Patterns and Regularities in Ontologies

  • Christian Kindermann

Student thesis: Phd


Knowledge representation languages often only define a fixed set of features for modelling knowledge and do not offer ways for extending this set of features. However, the provided features do not always lend themselves to capture complex conceptual models. As a result, one often has to combine available features in ingenious ways to solve non-trivial modelling problems. To communicate the purpose of such combinations in an intention revealing manner, it would be advantageous to encapsulate and structure them in terms of higher-level modelling constructs. This motivates the introduction of high-level languages for knowledge representation languages. In analogy to high-level programming languages that abstract over low-level features of machine languages, high-level modelling languages abstract over low-level features of knowledge representation languages. In particular, recurring modelling solutions, that combine low-level language features in some specific manner can be specified as reusable patterns in a high-level modelling language. This thesis is about such patterns and regularities in ontologies. Its primary focus is the analysis of patterns and regularities in terms of formally specified qualities and properties. This formal treatment provides the foundation for precise statements and hypotheses that can be verified and tested empirically. Frameworks, methodologies, and techniques for detecting, discovering, and encoding patterns and regularities in ontologies are presented. They are used to drive large-scale empirical investigations on patterns and regularities in biomedical ontologies. These investigations suggest that many ontologies follow a complex design based on highly interrelated patterns and regularities. The automated identification of such patterns and regularities as well as the analysis of their interrelations open up possibilities for pattern-based services that have a great potential for improving ontology comprehension and maintenance in practice.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorUli Sattler (Supervisor) & Bijan Parsia (Supervisor)


  • ontology engineering
  • OWL
  • macro language
  • syntactic regularity
  • ontology design pattern
  • knowledge representation
  • ontology template

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