Knowledge and Data

2019-2020

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 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
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.

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 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 Methods

The 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 Assessment

The 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.

Literature

Recommended: A Semantic Web Primer (3rd edition) Grigoris Antoniou, Paul
Groth, Frank van Harmelen and Rinke Hoekstra,
MIT Press, September 2012

Target Audience

B Econometrics & Operations Research (elective course)
Minor Bioinformatics & Systems Biology (elective course)
Flexible Minor
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

Recommended background knowledge

Basic programming (Python, Javascript), Web development, (Formal)
Modeling (Basic propositional and predicate logic)

General Information

Course Code X_400083
Credits 6 EC
Period P1
Course Level 300
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. K.S. Schlobach
Examiner dr. K.S. Schlobach
Teaching Staff dr. K.S. Schlobach
dr. ing. J. Raad

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Computer lab, Lecture
Target audiences

This course is also available as: