Knowledge and Data

Doel vak

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 be able to model data and knowledge and to develop a data science pipeline 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, SPARQL)
• 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.

Inhoud vak

In this course, we study formalisms that are useful and necessary to represent knowledge and data, in particular when these knowledge and data are to be reused, e.g. published and consumed on the Web. We introduce the concept of Knowledge Graphs, the technologies and representation formats (RDF, RDFS, OWL) for expressing semantics and linked data in a web-accessible format, use the SPARQL query language to query over this data. We finally build a data science application that uses integrated data for some intelligent task.

Even though content on the web is generally produced from structured data sources (databases), its representation is in a form that is meant for human consumption. Linked Data allows to scale the walls of this siloed information space, by reusing identifiers and vocabularies across these datasets, and presenting that information in a way that is appropriate for machine consumption.

Aanvullende informatie onderwijsvormen

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 work on a final project assignment. We schedule code interviews to verify that the learning objectives of the assignments are met. 

Regarding Generative AI use: this course operates at level 2b: Use of generative AI is permitted in a limited way, with disclosure

For the individual practical assignments, use of (generative) AI tools is not allowed.

For the final project assignment, the use of AI as a supportive tool towards the project document text (for brainstorming, improving written text etc) is allowed but should only be in support of, not as a replacement for these skills and there should be acknowledgement of use and reporting on how it affects the assignment. Use of such tools is not allowed for the other parts of the final project assignment (including knowledge engineering).

In case there is doubt about the originality/individuality of any contribution, we can ask for an oral check for that assignment.

Toetsvorm

The final grade will be determined by three components that each need to be passed to pass the course

• Weekly individual practical assignments (in total 5 assignments of which the results are averaged): 20% of the final grade
• A final project, assessed on the basis of a final document, application and (video) presentation: 15% of the final grade
• An exam testing the theory: 65% of the final grade

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.

Literatuur

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

Aanvullende informatie doelgroep

Bachelor Artificial Intelligence year 2

Aanbevolen voorkennis

• Basic programming (Python)
• Logic (Basic propositional and predicate logic)