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Results: 122
Number of items: 122
  • Open Access
    Allen, B. (2026). Neurosymbolic knowledge engineering with natural language. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Li, M., Zhang, P., Xing, W., Zheng, Y., Zaporojets, K., Chen, J., Zhang, R., Zhang, Y., Gong, S., Hu, J., Ma, X., Liu, Z., Groth, P., & Worring, M. (2026). A survey of large language models for data challenges in graphs. Expert Systems With Applications, 298(A), Article 129643. https://doi.org/10.1016/j.eswa.2025.129643
  • Open Access
    Li, X. (2026). From fine-tuning to prompting: A paradigm shift in knowledge graph construction. [Thesis, fully internal, Universiteit van Amsterdam].
  • Liberatore, T., & Groth, P. T. (2025). FashionDB [Data set]. Hugging Face. https://doi.org/10.57967/hf/6494
  • Brady, E., & Groth, P. (2025, July 11). Data Modeling in the Wild: A Corpus of Data Model Diagrams from Public GitHub Repositories [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15862889
  • Open Access
    Ayoughi, M., van Spengler, M., Mettes, P., & Groth, P. (2025). Designing Hierarchies for Optimal Hyperbolic Embedding. In E. Curry, M. Acosta, M. Poveda-Villalón, M. van Erp, A. Ojo, K. Hose, C. Shimizu, & P. Lisena (Eds.), The Semantic Web: 22nd European Semantic Web Conference, ESWC 2025, Portoroz, Slovenia, June 1–5, 2025 : proceedings (Vol. I, pp. 362-382). (Lecture Notes in Computer Science; Vol. 15718). Springer. https://doi.org/10.1007/978-3-031-94575-5_20
  • Open Access
    Li, X., Esposito, C. D., Groth, P., Sitruk, J., Szatmari, B., & Wijnberg, N. (2025). Evaluation of unsupervised static topic models’ emergence detection ability. PeerJ Computer Science, 11, Article 2875. https://doi.org/10.7717/peerj-cs.2875
  • Open Access
    Jackson, D., Groth, P., & Harmouch, H. (2025). BASIL DB: bioactive semantic integration and linking database. Journal of Biomedical Semantics, 16, Article 14. https://doi.org/10.1186/s13326-025-00336-3
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2025). mlidea: Interactively Improving ML Data Preparation Code via "Shadow Pipelines". Proceedings of the VLDB Endowment, 18(12), 5359–5362. https://doi.org/10.14778/3750601.3750671
  • Open Access
    Allen, B. P., Chhikara, P., Ferguson, T. M., Ilievski, F., & Groth, P. (2025). Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations. Proceedings of Machine Learning Research, 284, 392-419. https://proceedings.mlr.press/v284/allen25a.html
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