Semantic Web: Modeling and Ontologies

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Related lectures (32)

Page 3 of 4

Data Warehouses and Decision Support Systems

Explores views in data warehouses, materialization tradeoffs, top-k queries optimizations, and the impact of column stores.

In Silico Neuroscience: Data Reproducibility and Reusability

Emphasizes data reproducibility and reusability in in silico neuroscience, focusing on neuroinformatics tools and methods.

Distributed Information Systems: Overview and Models

Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.

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Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Handling Data: Intro to Pandas

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Entity-Relationship Model

Introduces the Entity-Relationship model for designing databases with a focus on conceptual and relational aspects.

Semi-Structured Data

Explores semi-structured data, embedding schema information to explain data values and relationships, discussing XML, schema-less data benefits, and serialization.