Ontology Engineering

The digital transformation of the industry depends on rich information models that are intelligible to both computers and humans. Such a model should represent how domain experts view their domain in order to enable them to view and explore the data they require. Constructing, maintaining, and using such models is far from straightforward however. The aim of this research program is to provide tools and methods to domain experts, information modellers, and ontology experts to improve the efficiency and quality of ontology development, maintenance, and use.

The digital transformation of the industry depends on rich information models that are intelligible to both computers and humans. These models should ideally represent the domain’s concepts and relationships in a manner to which domain experts are accustomed. This way users may explore and extract implicit information from data through the help of 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 straightforward. 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 artefact in order to express and make use of all the desired information artefacts, which in turn makes maintaining the ontology a real issue.

The aim of the ontology engineering research program is to develop tools and methods that improve the efficiency and quality of ontology development, maintenance and use in industry. These tools and methods are tailored to different users’ expertise and requirements, facilitating a separation of concerns where each user group can focus on what they know best: domain experts and programmers no longer need to have an in-depth understanding of logic and semantic technologies, whereas ontology experts and information modellers no longer need to become experts in the domain of interest. This is achieved 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 the ontology and integrating the ontology in existing software platforms
  • equipping ontology experts with powerful tools to oversee the development of the ontology

Reasonable Ontology Templates

Reasonable Ontology Templates (OTTR) is a language and framework for representing and instantiating recurring patterns within ontologies. It facilitates a modular design approach following the don’t-repeat-yourself principle. This ensures that any changes made to a pattern are automatically transferred to any of its instances within an ontology.

The feasibility and industrial scalability of this approach was demonstrated in collaboration with Aibel in a paper nominated for best research paper at the International Semantic Web Conference 2018. It showcases the use of templates on a large-scale industrial ontology. It also addresses the various open-source tools geared towards different user groups, as well as algorithms for sophisticated maintenance of ontologies.

OptiqueVQS (Visual Query System)

The OptiqueVQS is a visual query builder that (i) supports users in constructing queries over an ontology; (ii) evaluates the query over a SPARQL endpoint; (iii) displays the query results. It was originally developed in the EU project Optique as an integrated component of the Optique platform. The vision in the Ontology Engineering research program is to deliver a tool suite that enables end users to efficiently interact with and explore large repositories of semantic data and large-scale ontologies.

OptiqueVQS has now been reimplemented as a stand-alone open source application. Furthermore, a backend component for adaptive value suggestions has been designed and implemented. There has also been research published last year on ranking of user interface suggestions based on past queries.