Karyon: A scalable and easy to integrate ontology summarisation framework

Abstract

In the current Semantic Web Community, as the size and complexity of ontologies increase, ontology summarisation is becoming more important. There are many studies in the literature that use different approaches and metrics. However, many of these studies are not effective in terms of performance or have integration issues with current technologies. In this study, the popular ontology summarisation metrics are examined focusing on their performance in terms of time, and a number of metrics have been selected accordingly. To increase the accuracy of selections made with chosen metrics, we propose a novel metric: ‘name inclusion’. This metric promotes a concept if its name is subsumed by the name of another concept. As the existing summarisation applications have integration issues, we have implemented our summarisation framework to integrate easily with the latest web technologies. Therefore, the algorithm is implemented using Rust language, which performs well and easily integrates with other languages.

Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

“…Data driven methods have been widely used in industries [17,18,30,31], especially machine learning [26,29,[35][36][37]. The problem of transforming a bigger ontology to a smaller ontology of the same domain is often referred to as ontology modularisation [1][2][3][4]14] and ontology summarisation [19]. Most of them focus on the problem of selecting a subset of the ontology that is interesting for the users [12], but they still cannot avoid dummy entities.…”