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September 13-15, 2022, Vienna, Austria. Co-located with SEMANTiCS Conference 2022

AI approaches based on machine learning have become increasingly popular across all sectors. However, experience shows that AI initiatives often fail due to the lack of appropriate data or low data quality. Furthermore, state-of-the-art AI models are widely opaque and suffer from a lack of transparency and explainability. Semantic AI approaches combine methodology from statistical AI and symbolic AI based on semantic technologies such as knowledge graphs as well as natural language processing, while incorporating mechanisms for explainable AI. Semantic AI requires technical and organizational measures, which get implemented along the whole data lifecycle. While the individual aspects of semantic AI are being studied in their respective research communities a dedicated community focusing on their combination is yet to be established.

Important Dates

  • Submission Deadline: July 4, 2022
  • Notification of Acceptance: July 30, 2022
  • Camera-ready versions of accepted contributions: August 15, 2022
  • Workshop date: Tuesday, September 13, 2022

Topics of Interest

Topics relevant to this workshop include – but are not limited to – the following aspects of semantic AI:

  • Classification of types of emerging semantic AI approaches, e.g. those listed below
  • Novel design patterns for semantic AI systems
  • System development life-cycle stages when creating semantic AI systems
  • Neuro-symbolic AI, i.e., methods combining statistical AI based on machine learning and symbolic AI, based on semantic technologies
  • Machine teaching and other use of knowledge graphs in supervised machine learning to improve model quality and robustness
  • Semi-supervised learninng, i.e., combination of supervised and unsupervise machine learning, e.g., in natural language processing
  • Distant supervision, i.e., use of semantic rules and other heuristics for data labelling
  • Few- and zero-shot learning, i.e., adapting to unseen classes given only a few or no examples at all
  • Semantics-based approaches to explainable AI, human in the loop in AI to improve trustworthiness
  • Knowledge graphs and other semantic technologies for automated data quality management
  • Reports on benefits/limitations of combining machine learning and semantic technologies, possibly from the perspective of concrete application domains and tasks
  • Submission Guidelines

    We welcome the following types of contributions:

    • Research papers (max. 8 pages)
    • Best practices and lessons learned (max. 4 pages)

    All submissions must be written in English and adhere to the CEUR-ART style.

    Please use the following Overleaf template for LaTeX: https://www.overleaf.com/read/mwrbbrhrpwzv.

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