After successful completion of the course, students are able to demonstrate a deep understand of Knowledge Graphs.
In TISS all details are presented linearly on one page.
Learning outcomes are divided into three main blocks:
An overarching aim of the course is to understand the connections between Knowledge Graphs (KGs), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning and Data Science.
Learning outcomes (LOs) are structured into exactly these three blocks. A particular focus is of gaining a broad understanding of all of the following learning outcomes, coming from throughout the database, semantic web, machine learning and data science communities. Representations
The aim in this part is to understand and apply the predominant representations of knowledge and data in Knowledge Graphs.
This, crucially, includes the connections between using these types of knowledge in one Knowledge Graph.
Systems
The aim in this part is to be able to design and apply systems that manage Knowledge Graphs.
The latter two learning outcomes (together with LO11) provide a typical life-cycle of Knowledge Graphs: getting data into a KG, i.e., creating it (LO6), evolving a KG into a new one (LO7) and getting data out of a KG by providing services based on it (L11). Note that the term “life-cycle” is used loosely here, as in many Knowledge Graphs, providing applications is not necessarily the end of the life-cycle, but part of an on-going activity.
Applications
The aim of this part is to understand and design applications of Knowledge Graphs.
A particular focus here is getting a holistic understanding of the topics including their connections.
The course content is aligned to the learning outcomes (following the seminal didactical principle of constructive alignment). We here repeat them here and give details what material is covered in each.
Representations
To support a diversity of learning types, the course is offered in three tracks:
Of course, only one track needs to be completed to successfully complete the course. The live track will be held blocked and may be subject to COVID regulations and limitations of lecture rooms.
Standard and Extended Track
Supporting a diversity of learners, and in line with education literature that suggests inclusive design and a diversity of methods rather than individual intervention, learning is centred around covering all LOs form multiple perspectives.
Further details on the project portfolio can be found under "examination modalities".
Live Track
The live track replaces is based on the same lectures, however the project and project portfolio part is replaced by live "workshop-style" sessions:
We remark that availability and capacity limits of the live track will depend on the developing COVID conditions and regulations of the term. Participants will in any case be able to fully complete the course without any problems in the standard or extended track.
The ECTS breakdown is (from a total of 75 hours corresponding to 3.0 ECTS / 2.0h):
For the standard and extended track, summative assessment is by one item: the project portfolio, for the live track it is by the participation in the live workshops. The portfolio was already briefly mentioned in the "teaching methods" section. In particular, it:
The following is a (non-exhaustive) list of examples of what can make a good project and portfolio topic:
Each portfolio will have a particular focus, but needs to put it in the context of the other learning outcomes. E.g., while not every portfolio will apply KG Embeddings based models, it should put the covered topics in the context, to demonstrate meeting the learning outcomes.
The procedure is in three simple steps:
This will be supported by discussions and feedback throughout the course.
Grading is according to the following principles:
That is, it is not necessary for each portfolio to go beyond basic proficiency in all learning outcomes, but perfectly fine to excel in a selection of them.
The curricula information is currently being updated. Until this is the case we provide here the following information: