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When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.
A Knowledge Graph is a structured representation of knowledge used to provide a comprehensive and interconnected view of a specific domain. The FAIRification process of research data relies heavily on Knowledge Graphs, which serve as foundational elements of modern information systems. However, for individuals lacking a computer science background, comprehending the concept of Knowledge Graphs can be challenging, preventing them from creating or participating in its development. To overcome this, we suggest taking the OpenHPI course "Knowledge Graphs – Foundations and Applications" for anyone interested in the subject.
This programme will provide all the essential knowledge needed to design, execute, and utilise Knowledge Graphs. The course will concentrate on fundamental semantic technologies, such as the principles of knowledge representation and symbolic AI. This encompasses encoding information via RDF triples, representing knowledge through ontologies using OWL, performing efficient queries on Knowledge Graphs by means of SPARQL, expressing latent knowledge in vector spaces, and utilizing knowledge graphs in sophisticated information systems, such as semantic and exploratory search. Furthermore, this course will discuss the role of knowledge graphs in artificial intelligence and machine learning, as well as their potential to improve the explicability and trustworthiness of "black box" deep learning models such as Chat-GPT.
Participation is free of charge. Come and join us!