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    <loc>https://pacsoi.org/blog-2-1/the-project-team-semantics</loc>
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      <image:title>News - The project team @ SEMANTiCS</image:title>
      <image:caption>Maarten with his poster Putting the Athlete in Control of their Performance Data through Privacy-Preserving Aggregators across Data Vaults Athlete monitoring requires heterogeneous data from coaches, medical staff, psychologists, and activity-tracking devices amongst others. However, current systems are often centralized, limiting athlete data control, fine-grained access management, and data portability. This demo presents a semantic decentralized architecture in which athlete data remains in personal data vaults under athlete control. An Aggregator component is introduced that allows to efficiently compute reusable &amp; interoperable performance metrics from raw activity data and athlete settings. Using a modified version of the performance tracking application Elevate, we show how this set-up can support privacy-preserving, application-independent computation for athlete-centric performance monitoring.</image:caption>
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      <image:title>News - The project team @ SEMANTiCS</image:title>
      <image:caption>FnO-Convert: Automatic Semantic Annotation of ML Pipelines Using the Function Ontology Transparency in machine learning (ML) is increasingly critical in sensitive domains such as healthcare and finance. Mainly, prospective provenance, i.e., a description of what a process, such as an ML script, is intended to do, is essential for trust and accountability. Existing approaches are not fully automatic, requiring either manual provenance annotations or ongoing human intervention, making them time-consuming, error-prone, and unable to capture complete script functionality. We present FnO-Convert, an open-source Python application that automatically and exhaustively extracts PROV-O compliant prospective provenance from Python scripts without requiring developer annotations, using the Function Ontology (FnO), demonstrated here on machine learning (ML) pipelines. FnO-Convert recursively converts Python scripts into hierarchical FnO descriptions spanning script structure to individual operations. To demonstrate extensibility, Dockerfile support is included, showing that FnO’s separation of functional descriptions from their implementation environment allows descriptions across heterogeneous artifacts to be meaningfully linked. This demo demonstrates the FnO-Convert user interface, covering both the conversion process of the ML pipeline and the visualization of the generated RDF in a humanreadable form, enabling users to interactively explore the prospective provenance of the pipeline as well as how provided data flows through it, enhancing transparency</image:caption>
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    <lastmod>2025-09-29</lastmod>
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    <lastmod>2025-09-30</lastmod>
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