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Abstract
Semantic Web ontologies are widely used to provide a
conceptual schema for sharing and integrating data and knowledge using
a logic-based language. The content of ontologies may also be used to
provide background knowledge in machine learning models or provide
domain-specific constraints that can be verified automatically and
used for zero-shot predictions. The combination of embedding symbolic
representations (such as ontologies) and extracting symbolic
representations from the embeddings are two main components of
neuro-symbolic AI systems. I will introduce methods for embedding
Semantic Web ontologies and outline some of the properties of the
embeddings that relate to model and proof theory. I will further show
how the embeddings can be inverted to extract axioms and enable a form
of approximate reasoning. My main area of application is in life
sciences where several large ontologies have been developed, and I
will demonstrate the application of neuro-symbolic methods on the
prediction of protein functions using the Gene Ontology.
Brief Biography
Robert is an Associate Professor in Computer Science at King Abdullah
University of Science and Technology in Thuwal where he is the PI of
the Bio-Ontology Research Group (BORG). Prior to joining KAUST, Robert
held research positions at the Max Planck Institute for Evolutionary
Anthropology, the European Bioinformatics Institute, University of
Cambridge, and Aberystwyth University. He earned his PhD degree in
Computer Science from the University of Leipzig. Robert's research
focuses on the development and application of knowledge-based
algorithms in biology and biomedicine.