Afshin Sadeghi

PhD Student
Computer Science Institute
University of Bonn

Profiles: LinkedInGoogle ScholarDBLP, Twitter

Room 1.052
Endenicher Allee 19a, 53115 Bonn
University of Bonn, Computer Science

Short CV

Afshin Sadeghi is a PhD Student at the University of Bonn. Afshin’ research interests are in the area of Semantic Knowledge graphs and linked data, Machine Learning and Open Data.

Research Interests

  • Semantic Knowledge graphs and linked data,  
  • Machine Learning,
  • Open Data


  • MLwin



Sadeghi, Afshin; Shahini, Xhulia; Schmitz, Martin; Lehmann, Jens

BenchEmbedd: A FAIR Benchmarking tool forKnowledge Graph Embeddings Inproceedings

In: Joint Proceedings of the Semantics co-located events: Poster&Demo track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins co-located with Semantics 2021, Amsterdam and Online, September 6-9, 2021,, 2021.

Links | BibTeX

Sadeghi, Afshin; Malik, Hirra; Collarana, Diego; Lehmann, Jens

Relational Pattern Benchmarking on the Knowledge Graph Link Prediction Task Inproceedings

In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual, 2021.

Links | BibTeX

Sadeghi, Afshin; Collarana, Diego; Graux, Damien; Lehmann, Jens

Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities Inproceedings

In: Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II, pp. 548–564, Springer, 2021.

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Alam, Mirza Mohtashim; Nayyeri, Mojtaba; Xu, Chengjin; Rony, Md. Rashad Al Hasan; Yazdi, Hamed Shariat; Sadeghi, Afshin; Lehmann, Jens

The Effect of Rule Injectionin a Leakage Free Dataset Inproceedings

In: 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. 28–35,, 2020.

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Sadeghi, Afshin; Graux, Damien; Yazdi, Hamed Shariat; Lehmann, Jens

MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs Inproceedings

In: ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), pp. 1427–1434, IOS Press, 2020.

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Payrosangari, Samin; Sadeghi, Afshin; Graux, Damien; Lehmann, Jens

Meta-hyperband: Hyperparameter Optimization with Meta-learning and Coarse-to-Fine Inproceedings

In: Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II, pp. 335–347, Springer, 2020.

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Sadeghi, Afshin; Lehmann, Jens

Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter Inproceedings

In: Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I, pp. 622–630, Springer, 2019.

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Sadeghi, Afshin; Capadisli, Sarven; Wilm, Johannes; Lange, Christoph; Mayr, Philipp

Opening and Reusing Transparent Peer Reviews with Automatic Article Annotation Journal Article

In: Publ., vol. 7, no. 1, pp. 13, 2019.

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Sadeghi, Afshin; Lange, Christoph; Vidal, Maria-Esther; Auer, Sören

Integration of Scholarly Communication Metadata Using Knowledge Graphs Inproceedings

In: Research and Advanced Technology for Digital Libraries - 21st International Conference on Theory and Practice of Digital Libraries, TPDL 2017, Thessaloniki, Greece, September 18-21, 2017, Proceedings, pp. 328–341, Springer, 2017.

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Sadeghi, Afshin; Wilm, Johannes; Mayr, Philipp; Lange, Christoph

Opening Scholarly Communication in Social Sciences by Connecting Collaborative Authoring to Peer Review Journal Article

In: Inf. Wiss. Prax., vol. 68, no. 2-3, pp. 163, 2017.

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Sadeghi, Afshin; Fröhlich, Holger

Steiner tree methods for optimal sub-network identification: an empirical study Journal Article

In: BMC Bioinform., vol. 14, pp. 144, 2013.

Links | BibTeX