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Results: 121
Number of items: 121
  • Open Access
    Grafberger, S., Groth, P., Stoyanovich, J., & Schelter, S. (2022). Data distribution debugging in machine learning pipelines. VLDB Journal, 31(5), 1103-1126. https://doi.org/10.1007/s00778-021-00726-w
  • Open Access
    Harper, C. A., Daniel, R., & Groth, P. (2022). Question Answering with Additive Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines. In O. Corcho, L. Hollink, O. Kutz, N. Troquard, & F. J. Ekaputra (Eds.), Knowledge Engineering and Knowledge Management: 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26–29, 2022 : proceedings (pp. 51-65). (Lecture Notes in Computer Science; Vol. 13514), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-031-17105-5_4
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2022). Towards data-centric what-if analysis for native machine learning pipelines. In Proceedings of the Sixth Workshop on Data Management for End-to-End Machine Learning: in conjunction with the 2022 ACM SIGMOD/PODS Conference, Philadelphia, PA, USA Article 3 Association for Computing Machinery. https://doi.org/10.1145/3533028.3533303
  • Open Access
    Soiland-Reyes, S., Sefton, P., Crosas, M., Castro, L. J., Coppens, F., Fernández, J. M., Garijo, D., Grüning, B., La Rosa, M., Leo, S., Ó Carragáin, E., Portier, M., Trisovic, A., RO-Crate Community, Groth, P., & Goble, C. (2022). Packaging research artefacts with RO-Crate. Data Science, 5(2), 97-138. https://doi.org/10.3233/DS-210053
  • Open Access
    Thanapalasingam, T., van Berkel, L., Bloem, P., & Groth, P. (2022). Relational graph convolutional networks: a closer look. PeerJ Computer Science, 8, Article e1073. https://doi.org/10.7717/PEERJ-CS.1073
  • Open Access
    Soiland-Reyes, S., Bayarri, G., Andrio, P., Long, R., Lowe, D., Niewielska, A., Hospital, A., & Groth, P. (2022). Making Canonical Workflow Building Blocks interoperable across workflow languages. Data Intelligence, 4(2), Article 342–357. https://doi.org/10.5281/zenodo.5727730, https://doi.org/10.1162/dint_a_00135
  • Open Access
    Daza, D., Cochez, M., & Groth, P. (2022). SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning. In A. Vlachos, P. Agrawal, A. Martins, G. Lampouras, & C. Lyu (Eds.), Sixth Workshop on Structured Prediction for NLP: Proceedings of the Workshop : SPNLP 2022 : May 27, 2022 (pp. 32-39). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.spnlp-1.4
  • Daza Cruz, D., Cochez, M., & Groth, P. (2021). Inductive WN18RR and FB15k-237 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4501273
  • Nevin, J., Lees, M., & Groth, P. (2021, September 20). Network simulation algorithms [Data set]. Mendeley Data. https://doi.org/10.17632/fpjpznrbt2.2
  • Alam, M., Ali, M., Groth, P., Hitzler, P., Lehmann, J., Paulheim, H., Rettinger, A., Sack, H., Sadeghi, A., & Tresp, V. (Eds.) (2021). Machine Learning with Symbolic Methods and Knowledge Graphs: co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021) : Virtual, September 17, 2021. (CEUR Workshop Proceedings; Vol. 2997). CEUR-WS. http://ceur-ws.org/Vol-2997
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