Ontology Engineering

The digital transformation of the industry depends on rich information models in order to support
automation of specialized and knowledge intensive tasks.

Work in this research program is primarily performed in two
projects: the Reasonable Ontology Templates (OTTR) project and the Information Modelling Framework (IMF) project.

The digital transformation of the industry depends on rich information models in order to support automation of specialized and knowledge intensive tasks. These models must be intelligible and usable by both computers and humans and should ideally represent the concepts and relationships in a manner to which domain experts are accustomed. This way users and systems may explore and extract implicit information from data through the help of automated reasoning without the need for understanding the technical details of how and where the data is stored.

However, the construction, maintenance, and use of such a model, called an ontology, are far from straight forward. Creating and maintaining a high-quality ontology requires close collaboration between domain experts, information modellers, and ontology experts to ensure that the model works as intended. Furthermore, an ontology quickly becomes a very complex artifact in order to express and make use of all the desired information objects. This makes maintaining the ontology a real issue.

The aim of the ontology engineering research program is to develop tools and methods that improve the usability, efficiency and quality of ontology development, maintenance and use in the industry, by

  • lowering the barrier for domain experts to understand, build, and use ontologies without the support of ontology experts.
  • providing programmers and information modellers with powerful interfaces for interacting with and exploiting the knowledge captured in the ontology with existing software platforms.
  • equipping ontology experts with powerful tools to oversee the development of the ontology
Tags: semantic web, ontology, ontology engineering, knowledge representation
Published Aug. 18, 2020 10:08 AM - Last modified Oct. 4, 2022 5:32 PM