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Results: 121
Number of items: 121
  • Open Access
    Hulsebos, M., Demiralp, Ç., & Groth, P. (2023). GitTables: A Large-Scale Corpus of Relational Tables. Proceedings of the ACM on Management of Data, 1(1), Article 30. https://doi.org/10.1145/3588710
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Provenance Tracking for End-to-End Machine Learning Pipelines. In The ACM Web Conference 2023: Companion of the World Wide Web Conference WWW 2023 : April 30-May 4, 2023, Austin, Texas, USA (pp. 1512). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587557
  • Open Access
    Li, X., Hughes, A., Llugiqi, M., Polat, F., Groth, P., & Ekaputra, F. J. (2023). Knowledge-centric Prompt Composition for Knowledge Base Construction from Pre-trained Language Models. In S. Razniewski, J.-C. Kalo, S. Singhania, & J. Z. Pan (Eds.), Joint proceedings of the 1st workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM) and the 2nd challenge on Language Models for Knowledge Base Construction (LM-KBC): co-located with the 22nd International Semantic Web Conference (ISWC 2023) : Athens, Greece, November 6, 2023 Article 3 (CEUR Workshop Proceedings; Vol. 3577). CEUR-WS. https://ceur-ws.org/Vol-3577/paper3.pdf
  • Open Access
    Grafberger, S., Guha, S., Groth, P., & Schelter, S. (2023). Mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and Over? Proceedings of the VLDB Endowment, 16(12), 4002–4005. https://doi.org/10.14778/3611540.3611606
  • Open Access
    Soiland-Reyes, S., Goble, C., & Groth, P. (2023). Evaluating FAIR Digital Object and Linked Data as distributed object systems. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2306.07436
  • Open Access
    Allen, B. P., Stork, L., & Groth, P. (2023). Knowledge Engineering using Large Language Models. Transactions on Graph Data and Knowledge, 1(1), Article 3. https://doi.org/10.4230/TGDK.1.1.3
  • Open Access
    Hu, Q., Daza, D., Swinkels, L., Ūsaitė, K., 't Hoen, R.-J., & Groth, P. (2023). Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring. Paper presented at KDD Workshop: Fragile Earth: AI for Climate Sustainability - from Wildfire Disaster Management to Public Health and Beyond, Long Beach, California, United States. https://doi.org/10.48550/arXiv.2308.02622
  • Open Access
    Tamašauskaitė, G., & Groth, P. (2023). Defining a Knowledge Graph Development Process Through a Systematic Review. ACM Transactions on Software Engineering and Methodology, 32(1), Article 27. https://doi.org/10.1145/3522586
  • Open Access
    Jullien, S., Ariannezhad, M., Groth, P., & de Rijke, M. (2023). A Simulation Environment and Reinforcement Learning Method for Waste Reduction. Transactions on Machine Learning Research, 2023, Article 769. https://openreview.net/forum?id=KSvr8A62MD
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proceedings of the ACM on Management of Data, 1(2), Article 128. https://doi.org/10.1145/3589273
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