12,369 research outputs found

    DeepOnto: A Python Package for Ontology Engineering with Deep Learning

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    Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present Deeponto, a Python package designed for ontology engineering. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to include other essential components including reasoning, verbalisation, normalisation, projection, and more. Building on this module, Deeponto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methodologies, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of Deeponto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).Comment: under review at Semantic Web Journa

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    Challenges for Monocular 6D Object Pose Estimation in Robotics

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    Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surveys on object pose estimation establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, novel pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.Comment: arXiv admin note: substantial text overlap with arXiv:2302.1182

    a systematic review

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    Funding Information: This study is part of an interdisciplinary research project, funded by the Special Research Fund (Bijzonder Onderzoeksfonds) of Ghent University.Introduction: Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with ontologies, and the field can be difficult to navigate. This systematic review aims to (1) identify ontologies in the field of PA, (2) assess their content and (3) assess their quality. Methods: Databases were searched for ontologies on PA. Ontologies were included if they described PA or sedentary behavior, and were available in English language. We coded whether ontologies covered the user profile, activity, or context domain. For the assessment of quality, we used 12 criteria informed by the Open Biological and Biomedical Ontology (OBO) Foundry principles of good ontology practice. Results: Twenty-eight ontologies met the inclusion criteria. All ontologies covered PA, and 19 included information on the user profile. Context was covered by 17 ontologies (physical context, n = 12; temporal context, n = 14; social context: n = 5). Ontologies met an average of 4.3 out of 12 quality criteria. No ontology met all quality criteria. Discussion: This review did not identify a single comprehensive ontology of PA that allowed reuse. Nonetheless, several ontologies may serve as a good starting point for the promotion of PA. We provide several recommendations about the identification, evaluation, and adaptation of ontologies for their further development and use.publishersversionpublishe

    An ontology-based spatial group decision support system for site selection application

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    This paper presents a new ontology-based multicriteria spatial group decision support system (GDSS) dedicated to site selection problems. Site selection is one of the most complex problems in the construction of a new building. It presents a crucial problem in terms of selecting the appropriate site among a group of decision makers with multiple alternatives (sites); in addition, the site must satisfy several criteria. To deal with this, the present paper introduces an ontology based multicriteria analysis method to solve semantic heterogeneity in vocabulary used by participants in spatial group decision support systems. The advantages of using ontology in GDSS are many: i) it enables the integration of heterogeneous sources of data available on the web and ii) it enables to facilitate meaning and sharing of data used in GDSS by participants. In order to facilitate cooperation and collaboration between participants in GDSS, our work aims to apply ontology at the model's structuration phase. The proposed system has been successfully implemented and exploited for a personalized environment
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