Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 125
Number of items: 125
  • Open Access
    Nevin, J., Groth, P., & Lees, M. (2023). Data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion Models. In J. Mikyška, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2023: 23rd International Conference, Prague, Czech Republic, July 3–5, 2023 : proceedings (Vol. I, pp. 494-508). (Lecture Notes in Computer Science; Vol. 14073). Springer. https://doi.org/10.1007/978-3-031-35995-8_35
  • Open Access
    Daza, D., Alivanistos, D., Mitra, P., Pijnenburg, T., Cochez, M., & Groth, P. (2023). BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs. Journal of Biomedical Semantics, 14, Article 20. https://doi.org/10.1186/s13326-023-00301-y
  • Open Access
    Li, X., Polat, F., & Groth, P. (2023). Do Instruction-tuned Large Language Models Help with Relation Extraction? 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 15 (CEUR Workshop Proceedings; Vol. 3577). CEUR-WS. https://ceur-ws.org/Vol-3577/paper15.pdf
  • Open Access
    Karabulut, E., Degeler, V., & Groth, P. (2023). Semantic Association Rule Learning from Time Series Data and Knowledge Graphs. ArXiv. https://doi.org/10.48550/arXiv.2310.07348
  • 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
    Nevin, J., Groth, P., & Lees, M. (2023). An approach for analysing the impact of data integration on complex network diffusion models. Journal of complex networks, 11(4), Article cnad025. https://doi.org/10.1093/comnet/cnad025
  • 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
    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
    Gregory, K., Groth, P., Scharnhorst, A., & Wyatt, S. (2023). The Mysterious User of Research Data: Knitting Together Science and Technology Studies with Information and Computer Science. In K. Bijsterveld, & A. Swinnen (Eds.), Interdisciplinarity in the Scholarly Life Cycle: Learning by Example in Humanities and Social Science Research (pp. 191-211). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-11108-2_11
  • 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
Page 6 of 13