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 « 3 of 7 »

2022

Nayyeri, Mojtaba; Vahdati, Sahar; Khan, Md Tansen; Alam, Mirza Mohtashim; Wenige, Lisa; Behrend, Andreas; Lehmann, Jens

Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion Proceedings Article

In: Groth, Paul; Vidal, Maria-Esther; Suchanek, Fabian M.; Szekely, Pedro A.; Kapanipathi, Pavan; Pesquita, Catia; Skaf-Molli, Hala; Tamper, Minna (Ed.): The Semantic Web - 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29 - June 2, 2022, Proceedings, pp. 253–269, Springer, 2022.

BibTeX | Links:

Alam, Mirza Mohtashim; Rony, Md. Rashad Al Hasan; Ali, Semab; Lehmann, Jens; Vahdati, Sahar

Language Model-driven Negative Sampling Journal Article

In: CoRR, vol. abs/2203.04703, 2022.

BibTeX | Links:

2021

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

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain Journal Article

In: IEEE Access, vol. 9, pp. 116002–116014, 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: Neurocomputing, vol. 461, pp. 530–542, 2021.

BibTeX | Links:

Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Behrend, Andreas; Manthey, Rainer; Auer, Sören; Lehmann, Jens; Vahdati, Sahar

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade Journal Article

In: Scientometrics, vol. 126, no. 9, pp. 8129–8151, 2021.

BibTeX | Links:

34 entries « 3 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 « 3 of 7 »

2022

Nayyeri, Mojtaba; Vahdati, Sahar; Khan, Md Tansen; Alam, Mirza Mohtashim; Wenige, Lisa; Behrend, Andreas; Lehmann, Jens

Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion Proceedings Article

In: Groth, Paul; Vidal, Maria-Esther; Suchanek, Fabian M.; Szekely, Pedro A.; Kapanipathi, Pavan; Pesquita, Catia; Skaf-Molli, Hala; Tamper, Minna (Ed.): The Semantic Web - 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29 - June 2, 2022, Proceedings, pp. 253–269, Springer, 2022.

BibTeX | Links:

Alam, Mirza Mohtashim; Rony, Md. Rashad Al Hasan; Ali, Semab; Lehmann, Jens; Vahdati, Sahar

Language Model-driven Negative Sampling Journal Article

In: CoRR, vol. abs/2203.04703, 2022.

BibTeX | Links:

2021

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

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain Journal Article

In: IEEE Access, vol. 9, pp. 116002–116014, 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: Neurocomputing, vol. 461, pp. 530–542, 2021.

BibTeX | Links:

Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Behrend, Andreas; Manthey, Rainer; Auer, Sören; Lehmann, Jens; Vahdati, Sahar

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade Journal Article

In: Scientometrics, vol. 126, no. 9, pp. 8129–8151, 2021.

BibTeX | Links:

34 entries « 3 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 « 3 of 7 »

2022

Nayyeri, Mojtaba; Vahdati, Sahar; Khan, Md Tansen; Alam, Mirza Mohtashim; Wenige, Lisa; Behrend, Andreas; Lehmann, Jens

Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion Proceedings Article

In: Groth, Paul; Vidal, Maria-Esther; Suchanek, Fabian M.; Szekely, Pedro A.; Kapanipathi, Pavan; Pesquita, Catia; Skaf-Molli, Hala; Tamper, Minna (Ed.): The Semantic Web - 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29 - June 2, 2022, Proceedings, pp. 253–269, Springer, 2022.

BibTeX | Links:

Alam, Mirza Mohtashim; Rony, Md. Rashad Al Hasan; Ali, Semab; Lehmann, Jens; Vahdati, Sahar

Language Model-driven Negative Sampling Journal Article

In: CoRR, vol. abs/2203.04703, 2022.

BibTeX | Links:

2021

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

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain Journal Article

In: IEEE Access, vol. 9, pp. 116002–116014, 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: Neurocomputing, vol. 461, pp. 530–542, 2021.

BibTeX | Links:

Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Behrend, Andreas; Manthey, Rainer; Auer, Sören; Lehmann, Jens; Vahdati, Sahar

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade Journal Article

In: Scientometrics, vol. 126, no. 9, pp. 8129–8151, 2021.

BibTeX | Links:

34 entries « 3 of 7 »

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