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.

The process of developing ontologies – a formal, explicit specification of a shared conceptualisation – is addressed by well-known methodologies. As for any engineering development, its fundamental basis is the collection of requirements, which includes the elicitation of competency questions. Competency questions are defined through interacting with domain and application experts or by investigating existing datasets that may be used to populate the ontology i.e. its knowledge graph. The rise in popularity and accessibility of knowledge graphs provides an opportunity to support this phase with automatic tools. In this work, we explore the possibility of extracting competency questions from a knowledge graph. This reverses the traditional workflow in which knowledge graphs are built from ontologies, which in turn are engineered from competency questions. We describe in detail RevOnt, an approach that extracts and abstracts triples from a knowledge graph, generates questions based on triple verbalisations, and filters the resulting questions to yield a meaningful set of competency questions; the WDV dataset. This approach is implemented utilising the Wikidata knowledge graph as a use case, and contributes a set of core competency questions from 20 domains present in the WDV dataset. To evaluate RevOnt, we contribute a new dataset of manually-annotated high-quality competency questions, and compare the extracted competency questions by calculating their BLEU score against the human references. The results for the abstraction and question generation components of the approach show good to high quality. Meanwhile, the accuracy of the filtering component is above 86%, which is comparable to the state-of-the-art classifications.

Abstract In the music domain, several ontologies have been proposed to annotate musical data, in both symbolic and audio form, and generate semantically rich Music Knowledge Graphs. However, current models lack interoperability and are insufficient for representing music history and the cultural heritage context in which it was generated; risking the propagation of recency and cultural biases to downstream applications. In this article, we propose the Polifonia Ontology Network (PON) for music cultural heritage, centred around four modules: Music Meta (metadata), Representation (content), Source (provenance) and Instrument (cultural objects). We design PON with a strong accent on cultural stakeholder requirements and competency questions (CQs), contributing an NLP-based toolkit to support knowledge engineers in generating, validating, and analysing them; and a novel, high-quality CQ dataset produced as a result. We show current and future use of these resources by internal project pilots, early adopters in the music industry, and opportunities for the Semantic Web and Music Information Retrieval communities.

Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.

Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups, while being simple and fast to train and, as a result, easily transferable to a robotic platform.

Composing musical ideas longer than motifs or figures is still rare in music generated by machine learning methods, a problem that is commonly referred to as the lack of long-term structure in the generated sequences. In addition, the evaluation of the structural complexity of artificial compositions is still a manual task, requiring expert knowledge, time and involving subjectivity which is inherent in the perception of musical structure. Based on recent advancements in music structure analysis, we automate the evaluation process by introducing a collection of measures that can objectively describe structural properties of the music signal. This is done by segmenting music hierarchically, and computing our measures on the resulting hierarchies to characterise the decomposition process of music into its structural components. We tested our method on a dataset collecting music with different degrees of structural complexity, from random and computer-generated pieces to real compositions of different genres and formats. Results indicate that our method can discriminate between these classes of complexity and identify further non-trivial subdivisions according to their structural properties. Our work thus contribute a simple yet effective framework for the evaluation of music generation models in regard to their ability to create structurally meaningful compositions.

With advances in the field of machine learning, service robots are envisioned to become more present. The COVID-19 pandemic has accelerated this need. One such example would be coffee shops, which have become intrinsic to our everyday lives. Yet, serving an excellent cup of coffee is not trivial as a coffee blend typically comprises rich aromas, indulgent and unique flavours. Our work addresses this by proposing a computational model which recommends optimal coffee beans resulting from users' preferences. Given coffee properties (objective features), we apply different supervised learning techniques to predict coffee qualities (subjective features). We then consider an unsupervised learning method to analyse the relationship between coffee beans in the subjective feature space. Evaluated on a real coffee beans dataset based on digitised reviews, our results illustrate that the proposed computational model gives up to 92.7 percent recommendation accuracy for coffee prediction. From this, we propose how it can be deployed on a robot.