Achieving semantic interoperability across heterogeneous experimental data systems remains a major barrier to data-driven scientific discovery. The Analytical Information Markup Language (AnIML), a flexible XML-based standard for analytical chemistry and biology, is increasingly used in industrial R&D labs for managing and exchanging experimental data. However, the expressivity of the XML schema permits divergent interpretations across stakeholders, introducing inconsistencies that undermine the interoperability the AnIML schema was designed to support. In this paper, we present the AnIML Ontology , an OWL 2 ontology that formalises the semantics of AnIML and aligns it with the Allotrope Data Format to support future cross-system and cross-lab interoperability. The ontology was developed using an expert-in-the-loop approach combining LLM-assisted requirement elicitation with collaborative ontology engineering. We validate the ontology through a multi-layered approach: data-driven transformation of real-world AnIML files into knowledge graphs, competency question verification via SPARQL, and a novel validation protocol based on adversarial negative competency questions mapped to established ontological anti-patterns and enforced via SHACL constraints.

Ontology reuse is a complex process that requires the support of methodologies and tools to minimise errors and to keep the ontologies consistent and coherent. Although the vast majority of ontology engineering methodologies include a reuse phase, and reuse has been investigated for different tasks and purposes (e.g.ontology integration), this body of work does not seem to translate into practice, neither in the form of strict criteria for reuse, nor as a set of community proposed guidelines. In this paper, we report the salient results from a study aimed at ontology developers and practitioners, whose objective is to gain an insight into the gap between the theory and the practice of ontology reuse. Thefocus of our study is to gain practitioners' views on i) their preferred reuse approaches; ii) the types of ontologies they tend to reuse (e.g. specific domain ontologies or upper-level ontologies)iii) what reporting information they deem useful when deciding which ontology to reuse; iv) what are the main reasons deterring them from reusing an ontology. Our findings confirm and extend established results from the literature, but in addition, the study provides a fresh view on the practice of reuse with an explicit focus on highly experienced developers and moderately experienced ones. The study corroborates the need for a comprehensive set of recommendations, that are widely accepted by the community, and are possibly implemented in development tools.

Whilst Knowledge Graphs (KGs) are increasingly used in business scenarios, the construction of enterprise ontologies and the population of KGs from existing relational data remains a significant challenge. In this paper we report our experience in supporting CSols (an SME operating in the analytical laboratory domain) in transitioning their data from legacy databases to a bespoke KG. We modelled the KG using a streamlined approach based on state of the art ontology engineering methodologies, that addresses the challenges faced by SMEs when transitioning to new technologies: lack of resources to devote to the transition, paucity of comprehensive data governance policies, and resistance within the organisation to accepting new practices and knowledge. Our approach uses a combination of UML diagrams and a controlled language glossary to support stakeholders in reaching consensus during the knowledge capture phase, thus reducing the intervention of the ontology engineer only to cases where no agreement can be found. We present a case study illustrating the generation of the KG from a UML specification of part of the analytical domain and from legacy relational data, and we discuss the benefits and challenges of the approach.

Ontology modularization has received growing interest from the research community lately, since it supports tasks such as ontology design/reuse and knowledge selection and integration. Most of the research efforts have concentrated on approaches to extract modules, or generate partitions from an ontology. However these approaches are influenced by different definitions of ontology modularization and thus tend to vary w.r.t. the concepts and properties in the ontology that should define the module, and on the characteristics that modules should exhibit, which often depend on the task for which the modularization process is performed. This diversity of approaches makes the comparative evaluation of the output of different modularization processes hard to perform. In this paper, we propose an entropy inspired measure for modularization, Integrated Ontology Entropy, that approximates the information content of modules, and hence provides a profile for the module generated. This measure is independent of the modularization technique used, and is calculated as a function of the number of edges connecting the named concepts in the ontology, when a graph representation of the ontology is utilized. In the paper we apply this measure to different modularization techniques and we empirically show how the measure captures different characteristics of modules, such as the degree of redundancy and the level of connectedness.

In this paper we therefore propose a reformulation of the entropy metric to evaluate the amount of information carried by both the ontology structure, and also by the language elements (i.e. the semantics associated with the edges in the ontological graph). To evaluate this approach, the reformulated metric is empirically compared to Calemt & Daemi's original entropy metric, for a variety of different sized modules. The results suggest that not only can entropy differentiate between structurally different modules of the same size, but that our improved entropy metric provides a finer grain differentiation than the original entropy metric.

Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as introduction or removal of concept definitions, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase, and given the open nature of the semantic Web, introduction of new ontologies and modifications to existing ones are to be expected. Dynamically handling such changes, without requiring human intervention, becomes crucial. This paper presents a framework that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology.

Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in ModTool; a tool that produces ontology modules via extraction. Experiments were conducted to compare ModTool to other modularisation methods.