Robin Keskisärkkä

Huanyu Li, Els de Vleeschauwer, Robin Keskisärkkä et al.

Increasing the circularity of resource use in our society has been recognized as a path to sustainability, i.e., transitioning into a more circular economy. There are many different circular strategies to do so, such as reusing products and components, refurbishing and remanufacturing used products, or recycling left-over or used materials. To enable these strategies, it is necessary to share information at the infrastructure level and to communicate between industry sectors along the product life cycle. Enabling semantic interoperability in this information sharing and communication is therefore a key to increasing circularity. However, knowledge representation for the circular economy (CE) domain, which involves many relevant industry sectors related to product life cycles, remains challenging. To bridge this gap, we developed the Circular Economy Ontology Network (CEON) within the Onto-DESIDE project. This ontology network aims to fill gaps in CE by defining cross-sectorial concepts and to enable semantics-aware data documentation. We demonstrate CEON through cross-industry data documentation scenarios spanning construction, electronics, and textile sectors.

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä et al.

The ontology engineering process is complex, time-consuming, and error-prone, even for experienced ontology engineers. In this work, we investigate the potential of Large Language Models (LLMs) to provide effective OWL ontology drafts directly from ontological requirements described using user stories and competency questions. Our main contribution is the presentation and evaluation of two new prompting techniques for automated ontology development: Memoryless CQbyCQ and Ontogenia. We also emphasize the importance of three structural criteria for ontology assessment, alongside expert qualitative evaluation, highlighting the need for a multi-dimensional evaluation in order to capture the quality and usability of the generated ontologies. Our experiments, conducted on a benchmark dataset of ten ontologies with 100 distinct CQs and 29 different user stories, compare the performance of three LLMs using the two prompting techniques. The results demonstrate improvements over the current state-of-the-art in LLM-supported ontology engineering. More specifically, the model OpenAI o1-preview with Ontogenia produces ontologies of sufficient quality to meet the requirements of ontology engineers, significantly outperforming novice ontology engineers in modelling ability. However, we still note some common mistakes and variability of result quality, which is important to take into account when using LLMs for ontology authoring support. We discuss these limitations and propose directions for future research.

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä et al.

Ontology evaluation through functional requirements, such as testing via competency question (CQ) verification, is a well-established yet costly, labour-intensive, and error-prone endeavour, even for ontology engineering experts. In this work, we introduce OE-Assist, a novel framework designed to assist ontology evaluation through automated and semi-automated CQ verification. By presenting and leveraging a dataset of 1,393 CQs paired with corresponding ontologies and ontology stories, our contributions present, to our knowledge, the first systematic investigation into large language model (LLM)-assisted ontology evaluation, and include: (i) evaluating the effectiveness of a LLM-based approach for automatically performing CQ verification against a manually created gold standard, and (ii) developing and assessing an LLM-powered framework to assist CQ verification with Protégé, by providing suggestions. We found that automated LLM-based evaluation with o1-preview and o3-mini perform at a similar level to the average user's performance.