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Draft:Michael Färber

Michael Färber

Michael Färber (born 1987)[1] is a German computer scientist whose work focuses on artificial intelligence, natural language processing, knowledge graphs, large language models, and trustworthy AI.[2] Since April 2024, he has been professor of Scalable Software Architectures for Data Analytics at TU Dresden and department head for Cognitive AI at ScaDS.AI Dresden/Leipzig.[3]

Education and career

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Färber studied computer science and philosophy at Ulm University, completing a diploma degree in computer science in 2011 and a Bachelor of Arts degree in philosophy in 2012.[3] He received his doctorate from the Karlsruhe Institute of Technology in 2017 with a dissertation entitled Semantic Search for Novel Information.[3]

After receiving his doctorate, Färber was a Japan Society for the Promotion of Science fellow at Kyoto University. He later worked as a postdoctoral researcher at the University of Freiburg and at the Karlsruhe Institute of Technology.[3] From 2020 to 2024, he was acting W3 professor of Web Science at the Institute of Applied Informatics and Formal Description Methods at KIT.[2] In 2024, he moved to TU Dresden, where he was appointed professor at ScaDS.AI Dresden/Leipzig.[3]

Research

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Färber's research combines natural language processing, knowledge graphs, and machine learning.[2] His work includes large-scale scholarly knowledge graphs such as the Microsoft Academic Knowledge Graph, SemOpenAlex, and Linked Papers With Code.[4] He has also worked on explainable AI, Green AI, information retrieval, factuality, media analysis, and bias in AI systems.[2][5]

According to ScaDS.AI, Färber has published more than 120 scientific papers, including work in venues such as ACL, EMNLP, KDD, CIKM, ISWC, ECIR, ICML, NAACL, the Semantic Web Journal, and Scientometrics.[2]

Public engagement

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Färber has commented publicly on social and media-related questions concerning artificial intelligence. In 2021, he spoke in the Digiloglounge Digital series of the ZKM Center for Art and Media Karlsruhe about the use of AI for analysing bias in news texts.[6] In 2025, he was quoted in Die Welt and Berliner Zeitung on the energy demand of generative AI systems and language models.[7][8] In 2026, he appeared in the MDR programme Medien360G in an episode on AI chatbots, language models, and their social implications.[9]

Awards

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  • 2018: Semantic Web Outstanding Paper Award for the article Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO.[10]
  • 2022: Best Poster at the International Semantic Web Conference for GreenAI: An Ontology for Modeling the Energy Consumption of AI Models.[11]
  • 2023: Best Resource Track Paper Award at the International Semantic Web Conference for SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples.[12]

Selected publications

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  • Färber, Michael; Bartscherer, Frederic; Menne, Carsten; Rettinger, Achim (2018). "Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO". Semantic Web. 9 (1): 77–129. doi:10.3233/SW-170275.
  • Färber, Michael (2019). "The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data". The Semantic Web – ISWC 2019. Lecture Notes in Computer Science. Vol. 11779. Springer. pp. 113–129. doi:10.1007/978-3-030-30796-7_8.
  • Färber, Michael; Saier, Tarek (2020). "unarXive: a large scholarly data set with publications' full-text, annotated in-text citations, and links to metadata". Scientometrics. 125: 3085–3108. doi:10.1007/s11192-020-03382-z.
  • Färber, Michael; Ao, Lin (2022). "The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings". Quantitative Science Studies. 3 (1): 51–98. doi:10.1162/qss_a_00183.
  • Färber, Michael; Lamprecht, David; Krause, Johan; Aung, Linn; Haase, Peter (2023). "SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples". The Semantic Web – ISWC 2023. Lecture Notes in Computer Science. Springer. pp. 94–112. doi:10.1007/978-3-031-47243-5_6.
  • Yuan, Shuzhou; Nie, Erik; Färber, Michael; Schmid, Helmut; Schütze, Hinrich (2024). "GNNAVI: Navigating the Information Flow in Large Language Models by Graph Neural Network". Findings of the Association for Computational Linguistics: ACL 2024. arXiv:2402.08610.
  • Susanti, Yuni; Färber, Michael (2025). "Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery". Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. arXiv:2505.19407.

References

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  1. ↑ "Curriculum Vitae". Michael Färber's Research Group. Retrieved 24 April 2026.
  2. 1 2 3 4 5 "Prof. Dr.-Ing. Michael Färber". ScaDS.AI Dresden/Leipzig. Retrieved 24 April 2026.
  3. 1 2 3 4 5 "Die Fakultät begrüßt Prof. Michael Färber". Technische Universität Dresden. 29 April 2024. Retrieved 24 April 2026.
  4. ↑ "Michael Färber 0001". DBLP. Retrieved 24 April 2026.
  5. ↑ "Selected Publications". Michael Färber's Research Group. Retrieved 24 April 2026.
  6. ↑ "Digiloglounge Digital: Mit Künstlicher Intelligenz gegen Stimmungsmache". ZKM. 18 March 2021. Retrieved 24 April 2026.
  7. ↑ "Wenn ChatGPT und Co. eigene Atomkraftwerke brauchen". Die Welt. 18 July 2025. Retrieved 24 April 2026.
  8. ↑ "Künstliche Intelligenz: Die Pfannkuchenrezept-Recherche wird zum Energiefresser". Berliner Zeitung. 13 August 2025. Retrieved 24 April 2026.
  9. ↑ "Programmierte Nähe: Warum wir Chatbots vermenschlichen". MDR Medien360G. 2026. Retrieved 24 April 2026.
  10. ↑ "Semantic Web Outstanding Paper Award 2018". Semantic Web Journal. 10 October 2018. Retrieved 24 April 2026.
  11. ↑ "Awards". ISWC 2022. Retrieved 24 April 2026.
  12. ↑ "Awards". ISWC 2023. Retrieved 24 April 2026.
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