The details related to the development of my research in each of the above-mentioned career steps can be summarized as the following disciplines:

Knowledge-driven AI

Since 1st September 2023, I joined TU Dresden and the Scdas.AI center as research group. I mainly target to build up a group with a focus on the connection of knowledge graphs and large language models, and the role of Neuro-symbolic methods for future learning models. My position is funded with additional two doctoral students with whom I am exploring the above-mentioned topics.

Representation Learning

Since I joined InfAI in 2020 a research group leader, I worked on my vision of Nature-Inspired Machine Intel-ligence. We acquired group budget through EKFZ funding, and EU projects. I have been directing the group research activities, and research mainly on my vision for research in machine intelligence inspired by natural science that can result in innovations to address weaknesses of current ML approaches. The main activities of the group and planned research directions are focused on representation learning.

Learning and Reasoning in Large Scale Knowledge Graphs.

Shortly before finishing my dissertation, I became very interested in using link prediction techniques over the scholarly knowledge graph we had constructed by then. I used link prediction techniques to provide recommendations in the context of scholarly communication. Initially, I used graph partition approaches relying on semantic similarity measures to determine the relatedness between scholarly entities. I further continued with this line of research by using knowledge graph embedding (KGE) models in several other use cases, as well proposing new models. This is still a focal research direction of myself and my group.

Knowledge Graph-empowered Intelligent Information Systems

The core of my PhD topic was to explore the challenges and approaches for automated knowledge acquisition and curation, integration and management of heterogeneous metadata on the Web towards. This research has been conducted using the example of scholarly metadata towards a collaborative construction and management of a science knowledge graph. The aim was to facilitate the integrated use of different knowledge-aware AI-based methods, analytical techniques, and tools for improving scholarly communication. My research contributions provide useful approaches by following the FAIR data principles and providing metadata in a findable, accessible, interoperable, and reusable format. Efficient and scalable methods for integrating large amounts of data, as well as knowledge representation and discovery, were key challenges that I tackled. As a major part of my research activities, I constructed a specific knowledge graph for which I also provided quality-based assessments and meta research analytics by applying data mining and link discovery approaches.

Scientific publishing I published at venues that target core of artificial intelligence that deals with knowledge representation, learning and management such as ECAI, AAAI, EMNLP, IJCNN, IEEE Access, PAKDD, EDBT, ILP, ESWC, EKAW, TPDL, ICSC, MTSR, ECIR, and SEMANTiCS. I value collaborative research work and have conducted research not only with members of the Computer Science department at the University of Bonn and University of Oxford but also with the Knowledge Media Institute at the Open University London – UK, the CNR research council of Italy, Institute Mihajlo Pupin, Belgrade in Serbia, the University Hospital Dresden, the University Hospital Leipzig, the L3S Research Center in Hanover, and University of Vienna . Community involvement and Event Organization Active community involvement is very rewarding and beneficial for increasing the impact of science in general. I am the co-general chair of SEMANTICS conference with approximately 500 visitors per year.