Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.
Introduces semantic modelling through tabular data and RDF, covering relational databases, schema migration, future-proof schemata, SPARQL querying, and metaknowledge limitations.
Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
