DBIS

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

Empathic computing investigates how technology can support collaboration, shared understanding and human-centered interaction. In this lab, student teams design and develop their own projects using immersive and pervasive media—ranging from XR headsets and smartphones to tablets, laptops, and wearable devices. Projects may leverage open standards like WebXR, or use engines such as Unity, depending on the goals and platforms chosen. The focus is on creating meaningful, responsive experiences through thoughtful interaction design and emerging technologies.

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.

Modern web applications centralize data, creating isolated silos, whereas decentralized data ecosystems enable users to store and manage their own data, granting true data sovereignty. In this lab, small teams will tackle distinct challenges, such as ingesting wearable sensor streams, processing fitness metrics, or sharing activity logs with friends, to build components of a fully decentralized fitness‑tracking app that leverages the Solid framework for secure, user‑owned data exchange.

Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.

Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.