Jens Lehmann (scientist)

Jens Lehmann is a computer scientist, most noted for his work on knowledge graphs and Artificial Intelligence [1][2]. He is a full professor at the University of Bonn, Germany, and leading the Smart Data Analytics research group. He is also a Lead Scientist for Conversational AI and Knowledge Graphs at Fraunhofer IAIS.

Jens Lehmann
Born
March 29, 1982, Meissen, Saxony, Germany
NationalityGerman
Scientific career
FieldsKnowledge Graphs, Conversational AI, Large-scale Data Analytics (Big Data), Relational Learning
InstitutionsUniversity of Bonn, University of Leipzig, University of Oxford, Fraunhofer IAIS, Institute for Applied Informatics (InfAI)
Websitehttp://jens-lehmann.org

Research

In 2007, he co-founded the DBpedia knowledge graph project[2][3][4]. He also works on Symbolic Artificial Intelligence, in particular for learning concepts in Description Logics (DLs) as well as OWL class expressions[5][6].

Within the field of Question Answering, he developed approaches to transform natural language questions into queries against a knowledge graph[7]. Furthermore, he investigates representation learning approaches for knowledge graphs and their application in downstream machine learning tasks [8][9]. In 2020, Jens Lehmann was awarded in Germany with a honorary mention as among top 10 most influential researchers of AI in the last decade [10].

Jens Lehmann is coordinating the Fraunhofer IAIS Dresden lab with a main focus Conversational AI[11]. This includes projects presented at Hannover fair 2019[12] with Deutsche Telekom, winning the SPEAKER project, CEE AI.

Education and Career

Jens Lehmann graduated with a master's degree in Computer Science from the Technical University of Dresden and the University of Bristol in 2006. He then obtained a doctoral degree (Dr. rer. nat) with grade summa cum laude at the Leipzig University in 2010[13] and was a research visitor at the University of Oxford. In 2013, he became a leader of the Agile Knowledge Engineering and Semantic Web research group (AKSW) at the Leipzig University. Subsequently he was appointed as professor at University of Bonn and Fraunhofer IAIS in 2015. In 2019, he started the Fraunhofer IAIS Dresden lab.

Awards

The impact of his research has been awarded in different ways by the community. He received the 10 Year SWSA Award[14] for his work on DBpedia together with other co-founders that was published at the International Semantic Web Conference, the Semantic Web Journal outstanding paper Award[15], ESWC 7-Year Most Influential Paper Award, Outstanding Paper Award Winner at the Literati Network Awards for Excellence 2013, the ISWC 2011 Best Research Paper Award, and the Journal of Web Semantics Most Cited Paper Award 2006–2010.

gollark: As always\*, my code is correct\*\*.
gollark: See, function calls are different to just directly passing a function.
gollark: ```luaparallel.waitForAny(dostuff, function() while true do local _, char = os.pullEvent "char" if char == "e" then return end endend)```(Excuse my indentation, I'm on mobile)
gollark: Two main options:- refactor the while loop so it just pulls events in a loop and handles keys appropriately- use parallel
gollark: You can't do that easily because CC is event driven.

References
  1. Concept learning in description logics using refinement operators, J. Lehmann and P. Hitzler, Machine Learning
  2. DBpedia: A nucleus for a web of open data, S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, Z. Ives, International Semantic Web Conference 2008, 722-735, the ISWC 10 Year Best Paper Award
  3. DBpedia – A crystallization point for the web of data, J. Lehmann et. al. (shared first author), Journal of Web Semantics, 7(3):154–165, 2009, Journal of Web Semantics 2006–2010 Award.
  4. DBpedia SPARQL benchmark – performance assessment with real queries on real data, M. Morsey, J. Lehmann, S. Auer, and A. Ngonga Ngomo, International Semantic Web Conference 2011, ISWC Best Paper Award.
  5. DL-Learner: Learning concepts in description logics, J. Lehmann, Journal of Machine Learning Research, 10:2639–2642, 2009.
  6. book. "Learning OWL class expressions". German National Library. Retrieved 8 August 2020.
  7. Template-based question answering over RDF data, C. Unger, L. Buhmann, J. Lehmann, A. Ngonga-Ngomo, D. Gerber, P. Cimiano, World Wide Web Conference 2012, 639-648.
  8. Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework, Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Galkin, M., Sharifzadeh, S., Fischer, A., Tresp, V. and Lehmann, J., arXiv preprint arXiv:2006.13365 (2020).
  9. PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Emebddings, Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Sharifzadeh, S., Tresp, V. and Lehmann, J., arXiv preprint arXiv:2007.14175 (2020).
  10. Aminer. "Aminer". AI 2000 Knowledge Engineering Most Influential Scholars. Retrieved 7 August 2020.
  11. Stelzer, Gerhard. "Wie intelligent ist KI?: Kommunikation mit Missverständnissen". Elektronik.de. Retrieved 7 August 2020.
  12. Lauer, Bernhard. "Smarter Sprachassistent mit Domänenwissen". dotnetpro. Retrieved 7 August 2020.
  13. Lehmann, Jens (2010-01-05). Learning OWL Class Expressions (PDF). Leipzig: Dissertation Universität Leipzig. p. 223. Retrieved 7 August 2020.
  14. ianhorr. "SWSA 10-year award 2017". Semantic Web Science Association. Retrieved 7 August 2020.
  15. Hitzler, Pascal. "Semantic Web journal Awards 2014". Semantic Web – Interoperability, Usability, Applicability an IOS Press Journal. Retrieved 7 August 2020.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.