Kategorie: ‘Labs’
Bulding Large Language Model Applications – Lab
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.
Data Visualisation and Analytics
This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.
Empathic Computing Lab
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.
Knowledge Graphs Lab
The Knowledge Graphs Lab offers a practical insight into structured semantic graphs, which model real-world entities and their complex relationships. By leveraging Knowledge Graphs (KGs), you can represent, integrate, and reason over heterogeneous information in a way that makes data more Findable, Accessible, Interoperable, and Reusable (FAIR).
Why Knowledge Graphs?
Knowledge Graphs power a wide range of applications—from enhancing search engine results (e.g., Google) and fuelling intelligent assistants (like Siri or Alexa) to driving recommendation systems and providing verifiable data backbones for Large Language Models (LLMs)—as they have proven to be scalable, flexible and extendable for storing heterogeneous knowledge across diverse domains.
What You Will Do
- Work in small groups to tackle real-world challenges.
- Design and implement software solutions – from semantically integrating diverse datasets into KGs to integrating KG data into application pipelines.
- Explore how KGs can improve downstream AI applications, such as enhancing the output of LLMs using approaches like GraphRAG
What You Will Gain
- Hands-on experience with popular frameworks for Knowledge Graphs and LLMs.
- Practical insights into building and leveraging KGs, including data modelling, query processing, and semantic integration.
- Teamwork and software development skills
By the end of this lab, you will have a deeper understanding of Knowledge Graph concepts, tools, and how the knowledge of KG can be integrated into real-world applications.
Software Projektpraktikum – Building Large Language Model Applications
Knowledge Graph Lab SS 2025
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)
Knowledge Graph Lab WS 2024
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)
Knowledge Graph Lab SS 2024
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)