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Results: 121
Number of items: 121
  • Thanapalasingam, T., van Krieken, E., Bloem, P., & Groth, P. (2023, April 13). IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8039857
  • Open Access
    Simperl, E., Groth, P., Staab, S., Sabou, M., Blomqvist, E., & Allen, B. (2023). Knowledge Engineering with Language Models and Neural Methods. Dagstuhl Reports, 12(9), 93-96. https://doi.org/10.4230/DagRep.12.9.60
  • Open Access
    Ayoughi, M., Mettes, P., & Groth, P. (2023). Self-Contained Entity Discovery from Captioned Videos. ACM Transactions on Multimedia Computing Communications and Applications, 19(5s), Article 177. https://doi.org/10.1145/3583138
  • 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
    Prieto, L., Den Boef, J., Groth, P., & Cornelisse, J. (2023). Parameter Efficient Node Classification on Homophilic Graphs. Transactions on Machine Learning Research, 2023, Article 640. https://openreview.net/forum?id=LIT8tjs6rJ
  • 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
    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
    Karabulut, E., Degeler, V., & Groth, P. (2023). Semantic Association Rule Learning from Time Series Data and Knowledge Graphs. In A. Waaler, E. Kharlamov, B. Zhou, A. Soylu, D. Kyritsis, D. Roman, O. Savkovic, & S. Staab (Eds.), Proceedings of the Second International Workshop on Semantic Industrial Information Modelling (SemIIM 2023) : co-located with the 22nd International Semantic Web Conference (ISWC 2023) : Greece, Athens, 7 November 2023 Article 3 (CEUR workshop proceedings; Vol. 3647). CEUR-WS. https://doi.org/10.48550/arXiv.2310.07348
  • 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
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