- The Art of Understanding

• From Numbers to Insights
• Data, Information, and Knowledge
• Natural Language
• What is Successful Communication?
• The Art of Understanding

- Natural Language Processing

• NLP and Basic Linguistic Knowledge
• NLP Applications, Techniques and Challenges
• How to evaluate an NLP Experiment?
• Tokenization and Word Normalisation
• Statistical Language Models (N-Gram Model)
• Naive Bayes Text Classification
• Distributional Semantics and Word Vectors

- Knowledge Graphs

• Knowledge Representations and Ontologies
• Resource Description Framework (RDF)
• Modeling with RDFS
• Querying RDF(S) with SPARQL
• Popular Knowledge Graphs - Wikidata and DBpedia
• Ontologies with the Web Ontology Language (OWL)
• Linked Data Quality Assurance with SHACL
• From Linked Data to Knowledge Graphs

- Basic Machine Learning

• Machine Learning Fundamentals
• Evaluation and Generalization Problems
• Linear Regression
• Decision Trees
• Unsupervised Learning
• Neural Networks and Deep Learning
• Word Embeddings
• Knowledge Graph Embeddings

- ISE Applications

• Knowledge Graph Completion
• Knowledge Graphs and Large Language Models
• Semantic and Exploratory Search
• Semantic Recommender Systems

Learning objectives:

• The students know the fundamentals and measures of information theory and are able to apply those in the context of Information Service Engineering.
• The students have basic skills of natural language processing and are enabled to apply natural language processing technology to solve and evaluate simple text analysis tasks.
• The students have fundamental skills of knowledge representation with ontologies as well as basic knowledge of Semantic Web and Linked Data technologies. The students are able to apply these skills for simple representation and analysis tasks.
• The students have fundamental skills of information retrieval and are enabled to conduct and to evaluate simple information retrieval tasks.
• The students apply their skills of natural language processing, Linked Data engineering, and Information Retrieval to conduct and evaluate simple knowledge mining tasks.
• The students know the fundamentals of recommender systems as well as of semantic and exploratory search.

• D. Jurafsky, J.H. Martin, Speech and Language Processing, 2nd ed. Pearson Int., 2009.
• A. Hogan, The Web of Data, Springer, 2020. 
• G. Rebala, A. Ravi, S. Churiwala, An Introduction to Machine Learning, Springer, 2019.