Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using (...) informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process. (shrink)

Sustainability in systems refers to applying sustainable principles and practices to create more resilient, efficient, and equitable systems that promote the well-being of people and the planet. Sustainability is an essential topic in contemporary software engineering, and its relationship with the characteristics and properties of a system or product called quality attributes is still an open question since each researcher has established their definition of sustainability in software. This has created diverse terms and concepts for distinct application environments and scopes, (...) creating ambiguity and misconceptions. This work defines a domain ontology of Sustainability in Software named SinSO to address these issues. SinSO was implemented in OWL, using competency-based questions to validate. The findings show that this proposal satisfies several quality and content requirements. Also, using Protégé and the Hermit reasoner, we verified that SinSO is consistent since the ontology statements are coherent and do not lead to conflicting or contradictory conclusions. In addition, competency questions allowed us to demonstrate that SinSO does fulfill its purpose. FOCA methodology allowed us to evaluate SinSO quality. Also, SinSO was used in two case studies, one about software for senior-citizen smart-home, and the other, a simulator to develop and test smart-city applications, achieving positive outcomes. To verify its accuracy, completeness, and maintainability, further evaluations of SinSO are needed in real case studies. We conclude that SinSO can significantly contribute to reducing ambiguity and enhancing comprehension in this area. Furthermore, SinSO can be an effective tool for engineers to recognize the concepts and relationships in the sustainable domain to consider in the systems development life cycle to build sustainable systems. (shrink)

Reuse of elements from existing ontologies in the construction of new ontologies is a foundational principle in ontological design. It offers the benefits, among others, of consistency and interoperability between such knowledge structures as well as sharing resources. Reuse is widely found within important collections of established ontologies, such as BioPortal and the OBO Foundry. However, reuse comes with its own potential problems involving ontological commitment, granularity, and ambiguity. Guidelines are proposed to aid ontology developers and curators in their prospective (...) reuse of content. These guidelines have been gleaned over years of practice in the ontology field. The guidelines are couched in experiential reports on designing and curating particular ontologies (e.g., EXACT and EXACT2) and using generally accepted approaches (e.g., MIREOT) in doing so. Various software tools to assist in ontology reuse are surveyed and discussed. (shrink)