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The EU’s Horizon Europe programme granted a budget of €4 million for three years for the ENEXA (Efficient Explainable Learning on Knowledge Graphs) project on explainable artificial intelligence. The UvA will receive €521,162. The INDElab of the Informatics Institute led by Paul Groth will focus on the creation of knowledge graph extraction pipelines and the co-construction of explanations in the project.

Artificial intelligence (AI) has become an integral part of our lives. It has given rise to smart assistants that take on tasks that would otherwise take humans a great deal of time and effort – in medicine, business and industry, for example. To do this, smart assistants require vast amounts of data. ‘Knowledge graphs’ are one of the preferred mechanisms for representing data here, because they can be understood by both humans and machines and ensure that information is processed logically. They are considered key for a number of popular technologies such as Internet search engines and personal digital assistants. However, existing machine learning approaches for knowledge graphs still have some shortcomings, in particular with respect to scalability, consistency and completeness. A further problem is that they do not meet the human need for comprehensibility.

Copyright: UvA
Knowledge graphs are critical component of modern AI systems. We are excited to develop new technologies that can work with users to help them understand the results of machine learning over these knowledge graphs.

Partners

Besides the UvA are Paderborn University in Germany, the National Center for Scientific Research ‘Demokritos’ in Greece, the European Union Satellite Centre (SatCen) in Spain, as well as the companies DATEV and webLyzard technology involved in the ENEXA project.

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