Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.
Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.
Introduces semantic modelling through tabular data and RDF, covering relational databases, schema migration, future-proof schemata, SPARQL querying, and metaknowledge limitations.
Delves into Big Data in neuroscience, analyzing large datasets and addressing challenges in data organization, standardization, integration, and visualization.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.
