Nature-Inspired Machine Learning (NIMI)

PhD Supervisor

Nature-Inspired Machine Learning (NIMI)

Principal Scientist, Amazon AGI (Artificial General Intelligence)

Honorary Professor at TU Dresden
Member of InfAI

Prof. Dr. Jens Lehmann is a Principal Scientist at Amazon where he works at the AGI (Artificial General Intelligence) organisation on advancing large language models, conversational AI and knowledge graphs. He also holds an honorary professorship at TU Dresden, was selected as a fellow of ELLIS and is a member of  InfAI. His academic activities at TU Dresden and InfAI support the Smart Data Analytics research group. Previously, he was jointly appointed full professor at the University of Bonn and Fraunhofer IAIS leading approximately 40 researchers. In this role, he was a lead scientist at Fraunhofer IAIS and coordinated the Dresden branch of the institute. His research interests involve knowledge graphs, machine learning as well as question answering & dialogue systems. His main research goal is to investigate and build generally intelligent systems by combining knowledge- and data-driven approaches. Prof. Lehmann authored more than 200 articles in international journals and conferences winning 15 best paper awards and obtaining more than 25000 citations and h-index 60+. He is a supporter and contributor to community research projects, including DBpedia, DL-Learner and LinkedGeoData. Previously, he led the AKSW research group and completed his PhD with „summa cum laude“ at the University of Leipzig with visits to the University of Oxford.

34 entries « 5 of 7 »

2021

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning withLow-Dimensional Knowledge Graph Embeddings Journal Article

In: CoRR, vol. abs/2104.05003, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Rahman, Mahfuzur; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Vassilyeva, Nadezhda; Motta, Enrico; Lehmann, Jens

Trans4E: Link Prediction on Scholarly Knowledge Graphs Journal Article

In: CoRR, vol. abs/2107.03297, 2021.

BibTeX | Links:

2020

Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Lehmann, Jens; Vahdati, Sahar

3D Learning and Reasoning in Link Prediction Over Knowledge Graphs Journal Article

In: IEEE Access, vol. 8, pp. 196459–196471, 2020.

BibTeX | Links:

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning with Low Dimensional Knowledge Graph Embeddings Proceedings Article

In: Sallinger, Emanuel; Vahdati, Sahar; Nayyeri, Mojtaba; Wu, Lianlong (Ed.): Proceedings of the International Workshop on Knowledge Representation and Representation Learning co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020), Virtual Event, September, 2020, pp. 36–42, CEUR-WS.org, 2020.

BibTeX | Links:

Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören

Ontology Design for Pharmaceutical Research Outcomes Proceedings Article

In: Hall, Mark M.; Mercun, Tanja; Risse, Thomas; Duchateau, Fabien (Ed.): Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings, pp. 119–132, Springer, 2020.

BibTeX | Links:

34 entries « 5 of 7 »

Prof. Dr. Jens Lehmann

PhD Supervisor

Nature-Inspired Machine Learning (NIMI)

Principal Scientist, Amazon AGI (Artificial General Intelligence)

Honorary Professor at TU Dresden
Member of InfAI

Prof. Dr. Jens Lehmann is a Principal Scientist at Amazon where he works at the AGI (Artificial General Intelligence) organisation on advancing large language models, conversational AI and knowledge graphs. He also holds an honorary professorship at TU Dresden, was selected as a fellow of ELLIS and is a member of  InfAI. His academic activities at TU Dresden and InfAI support the Smart Data Analytics research group. Previously, he was jointly appointed full professor at the University of Bonn and Fraunhofer IAIS leading approximately 40 researchers. In this role, he was a lead scientist at Fraunhofer IAIS and coordinated the Dresden branch of the institute. His research interests involve knowledge graphs, machine learning as well as question answering & dialogue systems. His main research goal is to investigate and build generally intelligent systems by combining knowledge- and data-driven approaches. Prof. Lehmann authored more than 200 articles in international journals and conferences winning 15 best paper awards and obtaining more than 25000 citations and h-index 60+. He is a supporter and contributor to community research projects, including DBpedia, DL-Learner and LinkedGeoData. Previously, he led the AKSW research group and completed his PhD with „summa cum laude“ at the University of Leipzig with visits to the University of Oxford.

34 entries « 5 of 7 »

2021

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning withLow-Dimensional Knowledge Graph Embeddings Journal Article

In: CoRR, vol. abs/2104.05003, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Rahman, Mahfuzur; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Vassilyeva, Nadezhda; Motta, Enrico; Lehmann, Jens

Trans4E: Link Prediction on Scholarly Knowledge Graphs Journal Article

In: CoRR, vol. abs/2107.03297, 2021.

BibTeX | Links:

2020

Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Lehmann, Jens; Vahdati, Sahar

3D Learning and Reasoning in Link Prediction Over Knowledge Graphs Journal Article

In: IEEE Access, vol. 8, pp. 196459–196471, 2020.

BibTeX | Links:

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning with Low Dimensional Knowledge Graph Embeddings Proceedings Article

In: Sallinger, Emanuel; Vahdati, Sahar; Nayyeri, Mojtaba; Wu, Lianlong (Ed.): Proceedings of the International Workshop on Knowledge Representation and Representation Learning co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020), Virtual Event, September, 2020, pp. 36–42, CEUR-WS.org, 2020.

BibTeX | Links:

Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören

Ontology Design for Pharmaceutical Research Outcomes Proceedings Article

In: Hall, Mark M.; Mercun, Tanja; Risse, Thomas; Duchateau, Fabien (Ed.): Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings, pp. 119–132, Springer, 2020.

BibTeX | Links:

34 entries « 5 of 7 »

Publications

PhD Supervisor

Nature-Inspired Machine Learning (NIMI)

Principal Scientist, Amazon AGI (Artificial General Intelligence)

Honorary Professor at TU Dresden
Member of InfAI

Prof. Dr. Jens Lehmann is a Principal Scientist at Amazon where he works at the AGI (Artificial General Intelligence) organisation on advancing large language models, conversational AI and knowledge graphs. He also holds an honorary professorship at TU Dresden, was selected as a fellow of ELLIS and is a member of  InfAI. His academic activities at TU Dresden and InfAI support the Smart Data Analytics research group. Previously, he was jointly appointed full professor at the University of Bonn and Fraunhofer IAIS leading approximately 40 researchers. In this role, he was a lead scientist at Fraunhofer IAIS and coordinated the Dresden branch of the institute. His research interests involve knowledge graphs, machine learning as well as question answering & dialogue systems. His main research goal is to investigate and build generally intelligent systems by combining knowledge- and data-driven approaches. Prof. Lehmann authored more than 200 articles in international journals and conferences winning 15 best paper awards and obtaining more than 25000 citations and h-index 60+. He is a supporter and contributor to community research projects, including DBpedia, DL-Learner and LinkedGeoData. Previously, he led the AKSW research group and completed his PhD with „summa cum laude“ at the University of Leipzig with visits to the University of Oxford.

34 entries « 5 of 7 »

2021

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning withLow-Dimensional Knowledge Graph Embeddings Journal Article

In: CoRR, vol. abs/2104.05003, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Rahman, Mahfuzur; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Vassilyeva, Nadezhda; Motta, Enrico; Lehmann, Jens

Trans4E: Link Prediction on Scholarly Knowledge Graphs Journal Article

In: CoRR, vol. abs/2107.03297, 2021.

BibTeX | Links:

2020

Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Lehmann, Jens; Vahdati, Sahar

3D Learning and Reasoning in Link Prediction Over Knowledge Graphs Journal Article

In: IEEE Access, vol. 8, pp. 196459–196471, 2020.

BibTeX | Links:

Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning with Low Dimensional Knowledge Graph Embeddings Proceedings Article

In: Sallinger, Emanuel; Vahdati, Sahar; Nayyeri, Mojtaba; Wu, Lianlong (Ed.): Proceedings of the International Workshop on Knowledge Representation and Representation Learning co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020), Virtual Event, September, 2020, pp. 36–42, CEUR-WS.org, 2020.

BibTeX | Links:

Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören

Ontology Design for Pharmaceutical Research Outcomes Proceedings Article

In: Hall, Mark M.; Mercun, Tanja; Risse, Thomas; Duchateau, Fabien (Ed.): Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings, pp. 119–132, Springer, 2020.

BibTeX | Links:

34 entries « 5 of 7 »

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