General Information
| Course Code | X_400083 |
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| Credits | 6 EC |
| Period | P1 |
| Course Level | 300 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | dr. V. de Boer |
| Examiner | dr. V. de Boer |
| Teaching Staff |
dr. W.X. Wilcke drs. W.X. Wilcke MSc dr. V. de Boer B.B. Kruit B.B. Kruit |
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Part of programme(s)
Premaster
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar, Computer lab, Lecture |
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Target audiences
This course is also available as:
Course Objective
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.
Course Content
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. Google, Bing and Yahoo already use this type of linked, structured information to improve web search and information retrieval. But it also helps content providers, such as the BBC, to better augment their content with content from other sources (e.g. from Musicbrainz).
Additional Information 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.
Method of 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 practical assignments and project can not be re-taken.
Weekly intermediary quizzes will be used to provide formative feedback.
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
Additional Information Target Audience
Bachelor Artificial Intelligence year 2
Recommended background knowledge
- Basic programming (Python)
- Logic (Basic propositional and predicate logic)