Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.
Delves into Big Data in neuroscience, analyzing large datasets and addressing challenges in data organization, standardization, integration, and visualization.
Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.
