Machine learning and natural language processing

12 Machine learning for knowledge extraction from text


Lecture materials:

Pa164 lecture 12

QUESTIONS AND TASKS:

  • Basic principles of knowledge representation

  • Ontologies vs. knowledge graphs - pros and cons of each approach to knowledge representation

  • The stack of typical tasks in ontology learning

  • Main challenges and open problems of ontology learning

  • Techniques used for term extraction, synonym discovery and concept formation

  • Techniques used for taxonomy extraction

  • Techniques used for relation, rule and axiom extraction

  • Overview of a selected deep learning approach to knowledge extraction

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