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Integrating symbolic Artificial Intelligence (AI) with Machine Learning (ML) on Knowledge Graphs can enhance drug safety insights. Exploiting semantic web technologies like RDF/OWL with ML shows promising results.

Keywords:
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Area of Science:

  • Bioinformatics
  • Artificial Intelligence
  • Semantic Web Technologies

Background:

  • Artificial Intelligence (AI) and Machine Learning (ML) are increasingly applied across scientific domains.
  • Integrating symbolic AI with ML, especially on Knowledge Graphs, remains an underexplored area.
  • Leveraging semantic web technologies like RDF/OWL alongside ML offers potential for deeper insights.

Purpose of the Study:

  • To investigate the integration of symbolic AI (RDF/OWL semantics) with Machine Learning (ML) on Knowledge Graphs.
  • To explore the application of this integrated approach for enhancing drug safety analysis.
  • To demonstrate the utility of exploiting semantic web semantics within ML workflows.

Main Methods:

  • Developed an approach combining Machine Learning (ML) with RDF/OWL semantic reasoning on Knowledge Graphs.
  • Utilized signaling pathway data from the Reactome database as a specific use case.
  • Applied ML techniques to analyze semantic information for drug safety prediction.

Main Results:

  • The integrated approach yielded promising outcomes in exploring drug safety.
  • Demonstrated the value of exploiting RDF/OWL semantics within ML tasks.
  • The use case involving Reactome signaling pathways showed potential for uncovering novel insights.

Conclusions:

  • Integrating symbolic AI and ML on Knowledge Graphs is a promising direction for scientific discovery.
  • Exploiting RDF/OWL semantics in ML workflows can provide valuable insights, particularly in areas like drug safety.
  • Further research and collaboration with domain experts are recommended to fully realize the potential of this approach.