Course ObjectiveThe 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 Insights: Theory of Knowledge, Data and Information,
Knowledge Graphs, Semantic Web technology stack, Ontology Engineering,
Web Application Design
Application of Knowledge and Insights: Integration of acquired knowledge
in an intelligent semantic data driven web application.
Judgement: The ability to assess the value of available datasets and
ontologies for web applications, and to choose the appropriate
technology for a specific application.
Communication: The ability to write a report about a developed
Learning skills: The skill to acquire and apply knowledge and skills
about fundamental knowledge representation concepts as well as
state-of-the art technology.
Course ContentIn 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 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, and build a Web
application that uses the 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).
Teaching MethodsThe course consists of (interactive) lectures and lab sessions. Students
will work on individual assignments in the first half of the course.
They will also collaborate in groups for a final project assignment.
Method of AssessmentThe final grade will be determined by a grade for the foundational
material (individual assignments and partial exams) for 50% as well as
the final group project (report) 50%.
For the foundational part, there will be 5 digital exams and 5 practical
assignments in the first 5 weeks of the course. Students will need at
least a 5.5 on average for those 10 partial grades. Otherwise, there
will be a resit exam, which will take place in the examweek of the same
period. There will be NO resit for the final group project.
LiteratureRecommended: A Semantic Web Primer (3rd edition) Grigoris Antoniou, Paul
Groth, Frank van Harmelen and Rinke Hoekstra,
MIT Press, September 2012
Target AudienceB Econometrics & Operations Research (elective course)
Minor Bioinformatics & Systems Biology (elective course)
Minor Web Services and Data
B Information Sciences year 2
B Artificial Intelligence year 2
B Business Analytics (constrained choice)
B Artificial Intelligence year 2
Modeling (Basic propositional and predicate logic)
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. K.S. Schlobach|
|Examiner||dr. K.S. Schlobach|
dr. K.S. Schlobach
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Seminar, Computer lab, Lecture|
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