Knowledge and Data
Methods and technologies for expressing knowledge and data on the Web: RDF, RDFS, OWL, SPARQL, Linked Data, and Knowledge Graphs.
Learning objectives
The objective of the Knowledge and Data course is to make students acquainted with methods and technologies used for expressing knowledge and data, in particular on the Web. By the end of this course, students will have built an intelligent web application that queries and reasons over integrated knowledge from various sources obtained from the Web. All of this will be based on formal logic theory. Knowledge and understanding: at the end of the course, students will be familiar with: Theory of Data, Information and Knowledge Predictable inferencing and formal systems Linked Data and Knowledge Graphs Semantic Web technology stack (RDF, RDFS, OWL) Ontology Engineering Knowledge-driven Data Science Application of Knowledge and Insights: students will be able to: Represent knowledge and data in various formalisms (RDF, RDFS, OWL) Implement basic (RDFS) reasoning, Develop advanced knowledge models in RDFS and OWL Work with SPARQL for querying (distributed) knowledge graphs Integrate acquired knowledge in an intelligent semantic data driven application. Judgement: Students will be able to assess the value of available datasets and ontologies for web applications, and to choose the appropriate technology for a specific application. Communication: Students are able to write a report about a developed application. Learning skills: The skill to acquire and apply knowledge and skills about fundamental knowledge representation concepts as well as state-of-the art technology, both individually as in a group context.
This course covers 5 modules over 8 weeks: formal systems & knowledge graphs, RDF & SPARQL, RDFS & inferencing, OWL & ontology modeling, and ontology engineering & data integration. Assessment is split into three equal parts: weekly individual assignments (5 assignments), a multiple choice exam covering theory from weeks 1–5, and a final group project. For 2 of the 5 practical assignments there are mandatory one-on-one coding interviews with a TA where you must explain your code. Weekly formative quizzes provide practice throughout the course.
Assessment
The final grade will be determined by three components that each count for 1/3 of the final grade: Weekly individual practical assignments (in total 5 assignments of which the results are averaged) A final group project, assessed on the basis of a final document, application and (video) presentation An exam testing the theory Each component should be passed (5.5 or higher) in order to pass the course. There will be a resit option for the exam. The project can be resit only if a 4.5 or higher has been achieved. The practical assignments cannot be re- taken. Weekly intermediary quizzes will be used to provide formative feedback.
Teaching methods
The course consists of lectures where theory is discussed and Working group sessions in which exercises related to the theory are discussed. Students will work on individual practical assignments in the first half of the course and will be supported in computer practicals. Students will also collaborate in groups for a final project assignment.
Literature
We will provide the (online) reading material through Canvas. Recommended literature: A Semantic Web Primer (3rd edition) Grigoris Antoniou, Paul Groth, Frank van Harmelen and Rinke Hoekstra, MIT Press, September 2012