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.

41 entries « 6 of 9 »

2021

Westphal, Patrick; Vahdati, Sahar; Lehmann, Jens

A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics Proceedings Article

In: Katzouris, Nikos; Artikis, Alexander (Ed.): Inductive Logic Programming - 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, pp. 266–281, Springer, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Xu, Chengjin; Yaghoobzadeh, Yadollah; Vahdati, Sahar; Alam, Mirza Mohtashim; Yazdi, Hamed Shariat; Lehmann, Jens

Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models Proceedings Article

In: Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Reddy, P. Krishna; Srivastava, Jaideep; Chakraborty, Tanmoy (Ed.): Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part III, pp. 77–89, Springer, 2021.

BibTeX | Links:

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:

41 entries « 6 of 9 »

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.

41 entries « 6 of 9 »

2021

Westphal, Patrick; Vahdati, Sahar; Lehmann, Jens

A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics Proceedings Article

In: Katzouris, Nikos; Artikis, Alexander (Ed.): Inductive Logic Programming - 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, pp. 266–281, Springer, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Xu, Chengjin; Yaghoobzadeh, Yadollah; Vahdati, Sahar; Alam, Mirza Mohtashim; Yazdi, Hamed Shariat; Lehmann, Jens

Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models Proceedings Article

In: Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Reddy, P. Krishna; Srivastava, Jaideep; Chakraborty, Tanmoy (Ed.): Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part III, pp. 77–89, Springer, 2021.

BibTeX | Links:

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:

41 entries « 6 of 9 »

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.

41 entries « 6 of 9 »

2021

Westphal, Patrick; Vahdati, Sahar; Lehmann, Jens

A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics Proceedings Article

In: Katzouris, Nikos; Artikis, Alexander (Ed.): Inductive Logic Programming - 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, pp. 266–281, Springer, 2021.

BibTeX | Links:

Nayyeri, Mojtaba; Xu, Chengjin; Yaghoobzadeh, Yadollah; Vahdati, Sahar; Alam, Mirza Mohtashim; Yazdi, Hamed Shariat; Lehmann, Jens

Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models Proceedings Article

In: Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Reddy, P. Krishna; Srivastava, Jaideep; Chakraborty, Tanmoy (Ed.): Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part III, pp. 77–89, Springer, 2021.

BibTeX | Links:

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:

41 entries « 6 of 9 »

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