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Informatics Institute Informatics Institute

Prof. P.T. (Paul) Groth

Faculty of Science
Informatics Institute
Visiting address
  • Science Park 904
  • Room number: L4.16
Postal address
  • Postbus 94323
    1090 GH Amsterdam
Contact details
Social media
  • Publications

    2025

    • Karabulut, E., Groth, P. T., & Degeler, V. O. (2025). 3K: Knowledge-Enriched Digital Twin Framework. In 14th International Conference on the Internet of Things (IoT 2024) workshops: International Workshop on Longevity in IoT Systems (LongevIoT) (pp. 188-193). ACM. https://doi.org/10.1145/3703790.3703834
    • Liberatore, T., Groth, P., Kackovic, M., & Wijnberg, N. (2025). Influence Beyond Similarity: A Contrastive Learning Approach to Object Influence Retrieval. In M. Alam, M. Rospocher, M. van Erp, L. Hollink, & G. Asefa Gesese (Eds.), Knowledge Engineering and Knowledge Management: 24th International Conference, EKAW 2024, Amsterdam, The Netherlands, November 26–28, 2024 : proceedings (pp. 35-52). (Lecture Notes in Computer Science; Vol. 15370), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-031-77792-9_3 [details]
      • Downloads
    • Polat, F., Tiddi, I., & Groth, P. (2025). Testing prompt engineering methods for knowledge extraction from text. Semantic Web, 16(2), Article 2. https://doi.org/10.3233/SW-2437 [details]

    2024

    • Allen, B., Polat, F., & Groth, P. (2024). SHROOM-INDElab at SemEval-2024 Task 6: Zero- and Few-Shot LLM-Based Classification for Hallucination Detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) (pp. 839-844). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.semeval-1.120
    • Carriero, V. A., Groth, P., Presutti, V., Kaffee, L.-A. (Ed.), Razniewski, S. (Ed.), & Vougiouklis, P. (Ed.) (2024). Empirical ontology design patterns and shapes from Wikidata. Semantic Web, 1–25. https://doi.org/10.3233/sw-243613
    • Daga, E., & Groth, P. (2024). Data journeys: Explaining AI workflows through abstraction. Semantic Web, 15(4), 1057-1083. https://doi.org/10.3233/sw-233407 [details]
    • Grafberger, S., Groth, P., & Schelter, S. (2024). Towards Interactively Improving ML Data Preparation Code via “Shadow Pipelines”. In Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning (pp. 7–11). (SIGMOD/PODS ’24). ACM. https://doi.org/10.1145/3650203.3663327
    • Groth, P., Blomqvist, E., & Sequeda, J. F. (2024). Editorial for the Special Issue on Knowledge Engineering. Journal of Web Semantics, 83, 100840. https://doi.org/10.1016/j.websem.2024.100840
    • Karabulut, E., Pileggi, S. F., Groth, P., & Degeler, V. (2024). Ontologies in Digital Twins: A Systematic Literature Review. Future Generation Computer Systems, 153, 442-456. https://doi.org/10.1016/j.future.2023.12.013 [details]
    • Li, X., & Groth, P. (2024). How different is different? Systematically identifying distribution shifts and their impacts in NER datasets. Language Resources and Evaluation. Advance online publication. https://doi.org/10.1007/s10579-024-09754-8
    • Nevin, J., Zhang, P., Dimitrov, D., Lees, M., Groth, P., & Dietze, S. (2024). Understanding the Impact of Entity Linking on the Topology of Entity Co-occurrence Networks for Social Media Analysis. In Knowledge Engineering and Knowledge Management (pp. 69–85). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-77792-9_5
    • Soiland-Reyes, S., Goble, C., & Groth, P. (2024). Evaluating FAIR Digital Object and Linked Data as distributed object systems. PeerJ Computer Science, 10, Article e1781. https://doi.org/10.7717/PEERJ-CS.1781 [details]

    2023

    • 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 [details]
    • 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 [details]
    • Cong, T., Sun, Z., Groth, P., Jagadish, H., & Hulsebos, M. (2023). Introducing the Observatory Library for End-to-End Table Embedding Inference. In NeurIPS 2023 Second Table Representation Learning Workshop Neural Information Processing Systems Foundation. https://openreview.net/forum?id=JIrTIMI5Yd
    • 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 [details]
    • Dinh, T. A., den Boef, J., Cornelisse, J., & Groth, P. (2023). E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes. In J. Wang, Y. He, T. N. Dinh, C. Grant, M. Qiu, & W. Pedrycz (Eds.), 23rd IEEE International Conference on Data Mining Workshops: 1-4 December 2023, Shanghai, China : proceedings (pp. 1084-1091). (ICDMW; Vol. 2023). IEEE Computer Society. https://doi.org/10.1109/ICDMW60847.2023.00142 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • Jullien, S., Ariannezhad, M., Groth, P., & Rijke, M. D. (2023). A Simulation Environment and Reinforcement Learning Method for Waste Reduction. Transactions on Machine Learning Research, (4), Article 769. https://openreview.net/forum?id=KSvr8A62MD [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • Polat, F., Tiddi, I., Groth, P., & Vossen, P. (2023). Improving Graph-to-Text Generation Using Cycle Training. In S. Carvalho, A. F. Khan, A. Ostroški Anić, B. Spahiu, J. Gracia, J. P. McCrae, D. Gromann, B. Heinisch, & A. Salgado (Eds.), Language, data and knowledge 2023: LDK 2023 : proceedings of the 4th Conference on Language, Data and Knowledge : 12-15 September 2023, Vienna, Austria (pp. 256-261). NOVA CLUNL. https://doi.org/10.34619/srmk-injj [details]
    • 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 [details]
    • 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.
    • 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. Advance online publication. https://doi.org/10.1145/3522586 [details]

    2022

    • Carriero, V. A., Groth, P., & Presutti, V. (2022). Towards improving Wikidata reuse with emerging patterns. In L.-A. Kaffee, S. Razniewski, G. Amaral, & K. S. Alghamdi (Eds.), Proceedings of the 3rd Wikidata Workshop 2022 : co-located with the 21st International Semantic Web Conference (ISWC2022) : Virtual Event, Hangzhou, China, October 2022 Article 2 (CEUR Workshop Proceedings; Vol. 3262). CEUR-WS. https://ceur-ws.org/Vol-3262/paper2.pdf [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • Groth, P., Vidal, M-E., Suchanek, F., Szekely, P., Kapanipathi, P., Pesquita, C., Skaf-Molli, H., & Tamper, M. (Eds.) (2022). The Semantic Web: 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29–June 2, 2022 : proceedings. (Lecture Notes in Computer Science; Vol. 13261). Springer. https://doi.org/10.1007/978-3-031-06981-9 [details]
    • 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 [details]
    • Schröder, M., Staehlke, S., Groth, P., Nebe, J. B., Spors, S., & Krüger, F. (2022). Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation. Journal of Biomedical Semantics, 13, Article 4. https://doi.org/10.1186/s13326-021-00257-x [details]
    • 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 [details]
    • 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. Advance online publication. https://doi.org/10.3233/DS-210053 [details]
    • 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 [details]

    2021

    • Alam, M., Groth, P., de Boer, V., Pellegrini, T., Pandit, H. J., Montiel, E., Rodríguez Doncel, V., McGillivray, B., & Meroño-Peñuela, A. (Eds.) (2021). Further with Knowledge Graphs: proceedings of the 17th International Conference on Semantic Systems, 6-9 September 2021, Amsterdam, The Netherlands. (Studies on the Semantic Web; Vol. 53). IOS Press. https://doi.org/10.3233/SSW53 [details]
    • Daza, D., Cochez, M., & Groth, P. (2021). Inductive entity representations from text via link prediction. In The Web Conference 2021: proceedings of the World Wide Web Conference WWW 2021 : April 19-23, 2021, Ljubljana, Slovenia (pp. 798-808). Association for Computing Machinery. https://doi.org/10.1145/3442381.3450141 [details]
    • Harper, C. A., Cox, J., Kohler, C., Scerri, A., Daniel, R., & Groth, P. (2021). SemEval-2021 Task 8: MeasEval -- Extracting Counts and Measurements and their Related Contexts. In A. Palmer, N. Schneider, N. Schluter, G. Emerson, A. Herbelot, & X. Zhu (Eds.), The 15th International Workshop on Semantic Evaluation (SemEval-2021): proceedings of the workshop : August 5-6, 2021, Bangkok, Thailand (online) (pp. 306-316). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.semeval-1.38 [details]
    • Hendriks, B., Groth, P., & van Erp, M. (2021). Recognizing and Linking Entities in Old Dutch Text: A Case Study on VOC Notary Records. In A. Weber, M. Heerlien, E. Gassó Miracle, & K. Wolstencroft (Eds.), Proceedings of the International Conference Collect and Connect: Archives and Collections in a Digital Age: Leiden, the Netherlands, November 23-24, 2020 (pp. 25-36). (CEUR Workshop Proceedings; Vol. 2810). CEUR-WS. http://ceur-ws.org/Vol-2810/paper3.pdf [details]
    • Koesten, L., Gregory, K., Groth, P., & Simperl, E. (2021). Talking datasets – Understanding data sensemaking behaviours. International Journal of Human-Computer Studies, 146, Article 102562. https://doi.org/10.1016/j.ijhcs.2020.102562 [details]
    • Lamprecht, A.-L., Palmblad, M., Ison, J., Schwämmle, V., Al Manir, M. S., Altintas, I., Baker, C. J. O., Ben Hadj Amor, A., Capella-Gutierrez, S., Charonyktakis, P., Crusoe, M. R., Gil, Y., Goble, C., Griffin, T. J., Groth, P., Ienasescu, H., Jagtap, P., Kalaš, M., Kasalica, V., ... Wolstencroft, K. (2021). Perspectives on automated composition of workflows in the life sciences. F1000Research, 10, Article 897. https://doi.org/10.12688/f1000research.54159.1 [details]
    • Li, X., Magliacane, S., & Groth, P. (2021). The Challenges of Cross-Document Coreference Resolution in Email. In K-CAP '21: Proceedings of the 11th Knowledge Capture Conference : December 2-3, 2021 : virtual event, USA (pp. 273-276). Association for Computing Machinery. https://doi.org/10.1145/3460210.3493573 [details]
    • Nevin, J., Lees, M., & Groth, P. (2021). The non-linear impact of data handling on network diffusion models. Patterns, 2(12), Article 100397. https://doi.org/10.1016/j.patter.2021.100397 [details]
    • Shroff, N., Vandenbussche, P.-Y., Moore, V., & Groth, P. (2021). Supporting ontology maintenance with contextual word embeddings and maximum mean discrepancy. In S. Ben Abbès, R. Hantach, P. Calvez, D. Buscaldi, D. Dessì, M. Dragoni, D. Reforgiato Recupero, & H. Sack (Eds.), Joint Proceedings of the 2nd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP 2021) & 6th International Workshop on Explainable Sentiment Mining and Emotion Detection (X-SENTIMENT 2021): co-located with co-located with 18th Extended Semantic Web Conference 2021 : Hersonissos, Greece, June 6th - 7th, 2021 (moved online) (pp. 11-19). (CEUR Workshop Proceedings; Vol. 2918). CEUR-WS. http://ceur-ws.org/Vol-2918/paper2.pdf [details]
    • Szarkowska, K., Moore, V., Vandenbussche, P.-Y., & Groth, P. (2021). Quality assessment of knowledge graph hierarchies using KG-BERT. In M. Alam, D. Buscaldi, M. Cochez, F. Osborne, D. Reforgiato Recupero, & H. Sack (Eds.), Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021): co-located with the 20th International Semantic Web Conference (ISWC 2021) : Virtual Conference, online, October 25, 2021 Article 1 (CEUR Workshop Proceedings; Vol. 3034). CEUR-WS. http://ceur-ws.org/Vol-3034/paper1.pdf [details]
    • West, R., Bhagat, S., Groth, P., Zitnik, M., Couto, F. M., Lisena, P., Meroño-Peñuela, A., Zhao, X., Fan, W., Yin, D., Tang, J., Shou, L., Gong, M., Pei, J., Geng, X., Zhou, X., Jiang, D., Ricaud, B., Aspert, N., ... Sephus, N. (2021). Summary of Tutorials at The Web Conference 2021. In The Web Conference 2021: companion of the World Wide Web Conference WWW 2021: April 19-23, 2021, Ljubljana, Slovenia (pp. 727–733). Association for Computing Machinery. https://doi.org/10.1145/3442442.3453701 [details]
    • Zeng, W., Zhao, X., Tang, J., Lin, X., & Groth, P. (2021). Reinforcement Learning-based Collective Entity Alignment with Adaptive Features. ACM Transactions on Information Systems, 39(3), Article 26. https://doi.org/10.1145/3446428 [details]
    • den Boef, J. B., Cornelisse, J., & Groth, P. (2021). GraphPOPE: Retaining structural graph information using position-aware node embeddings. In M. Alam, D. Buscaldi, M. Cochez, F. Osborne, D. Reforgiato Recupero, & H. Sack (Eds.), Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021): co-located with the 20th International Semantic Web Conference (ISWC 2021) : Virtual Conference, online, October 25, 2021 Article 3 (CEUR Workshop Proceedings; Vol. 3034). CEUR-WS. http://ceur-ws.org/Vol-3034/paper3.pdf [details]

    2020

    2019

    • Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2019). Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines. Journal of the Association for Information Science and Technology, 70(5), 419-432. https://doi.org/10.1002/asi.24165 [details]
    • Groth, P., Scerri, A., Daniel, R., & Allen, B. P. (2019). End-to-end learning for answering structured queries directly over text. In M. Alam, D. Buscaldi, M. Cochez, F. Osborne, D. Reforgiato Recupero, & H. Sack (Eds.), Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019): co-located with the 16th Extended Semantic Web Conference 2019 (ESWC 2019) : Portoroz, Slovenia, June 2, 2019 (pp. 57-70). (CEUR Workshop Proceedings; Vol. 2377). CEUR-WS. http://ceur-ws.org/Vol-2377/paper_7.pdf [details]

    2018

    • Groth, P., Koesten, L., Mayr, P., de Rijke, M., & Simperl, E. (2018). DATA:SEARCH'18 - Searching Data on the Web: Preface. In L. Dietz, L. Koesten, & S. Verberne (Eds.), Joint Proceedings of the First International Workshop on Professional Search (ProfS2018); the Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR); and the International Workshop on Data Search (DATA:SEARCH’18): co-located with (ACM SIGIR 2018) : Ann Arbor, Michigan, USA, July 12, 2018 (pp. 65-66). (CEUR Workshop Proceedings; Vol. 2127). CEUR-WS. http://ceur-ws.org/Vol-2127/preface-datasearch.pdf [details]
    • Groth, P., Koesten, L., Mayr, P., de Rijke, M., & Simperl, E. (2018). DATA:SEARCH'18 - Searching data on the web. In SIGIR #41 proceedings: Ann Arbor, Michigan, USA, 08-12, July 2018 (pp. 1419-1422). Association for Computing Machinery. https://doi.org/10.1145/3209978.3210195 [details]

    2015

    • Hoekstra, R., & Groth, P. (2015). PROV-O-Viz - Understanding the Role of Activities in Provenance. In B. Ludäscher, & B. Plale (Eds.), Provenance and Annotation of Data and Processes: 5th International Provenance and Annotation Workshop, IPAW 2014, Cologne, Germany, June 9-13, 2014 : revised selected papers (pp. 215-220). (Lecture Notes in Computer Science; Vol. 8628). Springer. https://doi.org/10.1007/978-3-319-16462-5_18 [details]
    • Wibisono, A., Bloem, P., de Vries, G. K. D., Groth, P., Belloum, A., & Bubak, M. (2015). Generating scientific documentation for computational experiments using provenance. In B. Ludäscher, & B. Plale (Eds.), Provenance and Annotation of Data and Processes: 5th International Provenance and Annotation Workshop, IPAW 2014, Cologne, Germany, June 9-13, 2014 : revised selected papers (pp. 168-179). (Lecture Notes in Computer Science; Vol. 8628). Springer. https://doi.org/10.1007/978-3-319-16462-5_13 [details]

    2014

    • Beek, W., Groth, P., Schlobach, S., & Hoekstra, R. (2014). A Web Observatory for the Machine Processability of Structured Data on the Web. In WebSci'14: proceedings of the 2014 ACM Web Science Conference: June 23-26, 2014, Bloomington, IN, USA (pp. 249-250). Association for Computing Machinery. https://doi.org/10.1145/2615569.2615654 [details]
    • Hoekstra, R., Groth, P., & Charlaganov, M. (2014). Linkitup: Semantic Publishing of Research Data. In V. Presutti, M. Stankovic, E. Cambria, I. Cantador, A. Di Iorio, T. Di Noia, C. Lange, D. R. Recupero, & A. Tordai (Eds.), Semantic Web Evaluation Challenge: SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014: revised selected papers (pp. 95-100). (Communications in Computer and Information Science; Vol. 475). Springer. https://doi.org/10.1007/978-3-319-12024-9_12 [details]

    2013

    2023

    2022

    • Soiland-Reyes, S., Castro, L. J., Garijo, D., Portier, M., Goble, C., & Groth, P. (2022). Updating Linked Data practices for FAIR Digital Object principles. Research Ideas and Outcomes, 8, Article e94501. https://doi.org/10.3897/rio.8.e94501
    • Soiland-Reyes, S., Sefton, P., Castro, L. J., Coppens, F., Garijo, D., Leo, S., Portier, M., & Groth, P. (2022). Creating lightweight FAIR Digital Objects with RO-Crate. Research Ideas and Outcomes, 8, Article e93937. https://doi.org/10.3897/rio.8.e93937

    2020

    2012

    • Antoniou, G., Groth, P., van Harmelen, F., & Hoekstra, R. (2012). A Semantic Web Primer. (3rd ed.) (Cooperative information systems). MIT Press. [details]

    2022

    • Groth, P., Rula, A., Schneider, J., Tiddi, I., Simperl, E., Alexopoulos, P., Hoekstra, R., Alam, M., Dimou, A., & Tamper, M. (Eds.) (2022). The Semantic Web: ESWC 2022 Satellite Events: Hersonissos, Crete, Greece, May 29–June 2, 2022 : proceedings. (Lecture Notes in Computer Science; Vol. 13384). Springer. https://doi.org/10.1007/978-3-031-11609-4 [details]

    2021

    • 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 [details]

    2023

    • Grafberger, S., Karlaš, B., Groth, P. T., & Schelter, S. (2023). Towards Declarative Systems for Data-Centric Machine Learning. Abstract from Data-Centric Machine Learning Research work-
      shop (DMLR) at ICML. https://dmlr.ai/assets/accepted-papers/41/CameraReady/autodc.pdf
    • Hu, Q., Daza, D., Swinkels, L., Usaite, K., 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

    2022

    • Soiland-Reyes, S., Sefton, P., Castro, L. J., Coppens, F., Garijo, D., Leo, S., Portier, M., Groth, P., & Goble, C. (2022). Creating lightweight FAIR Digital Objects with RO-Crate and FAIR Signposting. Poster session presented at 1st International Conference on FAIR Digital Objects , Leiden, Netherlands. https://doi.org/10.5281/zenodo.7245315

    2019

    • Symeonidou, A., Sazonau, V., & Groth, P. (2019). Transfer learning for biomedical named entity recognition with BioBert. Poster session presented at 15th International Conference on Semantic Systems, SEMPDS 2019, Karlsruhe, Germany. http://ceur-ws.org/Vol-2451/paper-26.pdf

    Prize / grant

    • van Noort, G. & Groth, P. (2019). Ethical MInDS: Mapping interventions for data use in squads.

    2025

    • Soiland-Reyes, S. (2025). FAIR Research Objects and computational workflows: A Linked Data approach. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2024

    • Hendriksen, M. Y. (2024). Multimodal machine learning for information retrieval: A vision and language perspective. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    • Hulsebos, M. (2024). Table Representation Learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    • Nevin, J. G. (2024). The ramifications of data handling for computational models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2023

    • Karabulut, E., Pileggi, S., Groth, P. & Degeler, V. (21-7-2023). Data and Statistics for the SLR entitled "Ontologies in Digital Twins: A Systematic Literature Review". Zenodo. https://doi.org/10.5281/zenodo.8172341
    • Thanapalasingam, T., van Krieken, E., Bloem, P. & Groth, P. (13-4-2023). IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. Zenodo. https://doi.org/10.5281/zenodo.8039857
    • Thanapalasingam, T., van Krieken, E., Bloem, P. & Groth, P. (4-2023). IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. Zenodo. https://doi.org/10.5281/zenodo.14787483

    2022

    2021

    2020

    2019

    2013

    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Ancillary activities
    • Springer Nature
      Book series editor for the Synthesis Lectures on the Semantic Web
    • longform.ai
      co-founder of a spin-out from the University of Amsterdam
    • MIT Press
      Text book author
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