Kategorie: ‘Courses’
Knowledge Graph Lab SS 2026
Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.
Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.
Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.
The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).
In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:
- Graph representation of data
- Knowledge Graph basics
- Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
- Use of vocabularies and ontologies as schemas for graphs
- Searching information in knowledge graphs
- Information extraction into knowledge graphs
- Data mining techniques for knowledge graphs
- Knowledge graph completion (predicting links, finding anomalies)
- Data governance aspects, e.g., data quality
- Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)
- GraphRAG
- Application of AI-enhancements along the KG-pipeline
Mixed Reality Lab
Mixed Reality combines virtual, augmented, and extended reality into a continuum of spatial computing experiences on devices such as the Meta Quest 3, Microsoft HoloLens 2, HTC Vive Pro, and Android smartphones. In this practical Bachelor lab, students in teams of 3-4 design and implement their own mixed reality application using modern toolchains (e.g., Unity and OpenXR). Participants learn key MR interaction patterns, input and UX design, and deployment to target devices. Registration for the lab is handled via SuPra; late registrants may join a waiting list.
Intelligent Data Management with Generative AI
Generative AI unlocks new potentials supporting tasks in many fields – including data management. It can significantly enhance data management by automating and improving tasks across the data lifecycle. In research data management, it supports metadata creation, documentation, and data curation. For database modeling, it enables schema generation from natural language, query formulation, and schema evolution. In data integration, it assists with semantic mapping and knowledge graph construction. It also can contribute to data quality and governance through supporting anomaly detection and policy drafting and may enhance data ecosystems by aiding cataloging and data exchange. However, the integration of generative AI into data management also presents challenges—ranging from resource efficiency and result quality to issues like bias, hallucination, and data privacy. In this seminar, you will explore a specific subtopic within this field, deepening your understanding while developing your academic writing and presentation skills.
AI Self-Reflection – Understanding How LLMs Learn from Themselves
As Large Language Models (LLMs) become more capable, researchers and practitioners are exploring whether these systems can reflect on their own reasoning, identify their errors, and improve independently.
This emerging area of AI self-reflection goes beyond self-correction. It focuses on a model’s capacity not only to revise its answers but also to reason about its own reasoning process, analyze the underlying causes of errors, and adapt future strategies.
Self-reflection is a crucial step towards trustworthy, transparent, and context-adaptive AI systems. It is becoming increasingly important in academic research and industrial applications such as AI quality assurance, error diagnosis, and model alignment.
Bridge Course Databases
A blended learning bridge course for master’s students in Data Science, Computational Social Science, and related programs.
Datenbanken und Informationssysteme
Die Vorlesung “Datenbanken und Informationssysteme” gibt einen einführenden Überblick über Datenbanken und ihre Verwendung in Informationssystemen.
Bridge Course Databases
A blended learning bridge course for master’s students in Data Science, Computational Social Science, and related programs.
Research Focus Class: Industrial Applications for LLM-driven Agentic Systems
UPDATE: Unfortunately, we reached the limit of our employees capacity, therefore we are unable to provide more seats for this class. The application process is closed.
In this Research Focus Class (RFC), we would like to provide an environment for students to work on developing their own research ideas. With a guidance from research assistants, we will ask you to design and implement these ideas independently. Results of such classes may lead to identifying a Master’s thesis topic or publications in scientific venues. This class offers students the opportunity to gain hands-on experience through interactive and practical research in an innovative setting.
If you are interested in participating in this course, please send us an email (to rfc@dbis.rwth-aachen.de) with a brief description of your motivation for taking this course.
Image generated with AI.
Research Focus Class on Data Ecosystems
This research-oriented course is for students who are interested in current issues, developments, and research in the field of data ecosystems. A selected topic from one of the following areas of data ecosystems will be discussed: data sovereignty, data exchange, data protection, data security, FAIR data, etc.
First, there will be an introduction to the current state of research on the selected topic. Next, each student will be guided in identifying a question (research idea) within the topic area, familiarizing themselves with it, and presenting it to the other participants. This concept phase will be followed by a practical phase in which students will develop (prototype implementation, analysis, simulation, etc.) and evaluate their research idea. The exact procedure may vary from semester to semester and depending on the topic area.
Semantic Web
As part of the W3C Semantic Web initiative standards and technologies have been developed for machine-readable exchange of data, information and knowledge on the Web. These standards and technologies are increasingly being used in applications and have already led to a number of exciting projects (e.g. DBpedia, semantic wiki or commercial applications such as schema.org, OpenCalais, or Google’s Freebase). These technologies become even more important in the era of Large Language Models (LLMs) because they enable the integration and structuring of vast amounts of disparate data, making it more accessible and meaningful for AI systems to process and understand and grouding the LLMs output in facts, preventing hallucination.
In this course, you will gain hands-on experience with linked data technologies while exploring their theoretical background.