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Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Explores text mining of long-tail data in neuroscience and brain connectivity, including named entity recognition, protein concentration mining, and comparison of connectivity matrices.
Introduces semantic modelling through tabular data and RDF, covering relational databases, schema migration, future-proof schemata, SPARQL querying, and metaknowledge limitations.
Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.
Delves into the relationship between architecture, landscape, and territory, exploring critical reviews of architectural projects and the concept of ruins.
