A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The candidate has theoretical knowledge of basic concepts related to:

• standards and technologies for semantic information systems, for example using RDF and SPARQL
• semantic modelling of interoperable information systems, for example using RDFS and OWL
• available sources of and vocabularies for big, open, and semantically marked-up data

Skills

The candidate

• can develop advanced information models, for example ontologies
• can use formats and languages such as RDF, RDFS, OWL, SPARQL, and JSON-LD
• can use existing frameworks and tools for managing semantic data and metadata
• can use existing frameworks and tools for developing semantic applications

• Basic data skills in data management and artificial intelligence
• Medium level skills in programming

• INFO125 Data Management
• INFO132 Programming
• INFO135 Advanced Programming
• INFO180 Methods in Artificial Intelligence

Compulsory participation: Attendance at labs (at least 75 %)

Approved compulsory requirements are valid for the two following semesters.

Update: In the spring semester 2021, the requirement to attend 75% of the seminars will not apply due to the corona situation. However, it is highly recommended that students attend as much as possible.

• 3 hours written exam (60%)
• 1 group assignment (40%)