2024W

• Bachelor Thesis / 184.691 / PR
• Business Intelligence / 188.429 / VU
• Doctoral & Master Students Seminar / 181.223 / SE
• Introduction to Semantic Systems / 188.399 / VU
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for Master Students in Software Engineering & Internet Computing / 180.777 / SE
• Seminar in Theoretical Computer Science / 192.064 / SE

2025S

• Bachelor Thesis / 184.691 / PR
• Database Systems / 184.686 / VU
• Doctoral & Master Students Seminar / 181.223 / SE
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR

• Health virtual twins for the personalised management of stroke related to atrial fibrillation
  2024 – 2028 / European Commission
  Publications: 210251 / 210250 / 210252

2025

• A European network to develop virtual twin technology for personalized stroke management in atrial fibrillation: the TARGET consortium / Ortega-Martorell, S., Olier-Caparroso, I., Lip, G., Hose, K., Tomaszuk, D., & TARGET consortium. (2025). A European network to develop virtual twin technology for personalized stroke management in atrial fibrillation: the TARGET consortium. European Heart Journal, 46(3), 229–232. https://doi.org/10.1093/eurheartj/ehae673
  Project: TARGET (2024–2028)

2024

• AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations / Nematov, I., Sacharidis, D., Sagi, T., & Hose, K. (2024). AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations. arXiv. https://doi.org/10.34726/8224
  Download: AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations (4.02 MB)
• Data-driven PC-chair-in-the-loop Formation of Program Committees: An EDBT 2023 Experience / Saha Bhowmick, S., & Hose, K. (2024). Data-driven PC-chair-in-the-loop Formation of Program Committees: An EDBT 2023 Experience. SIGMOD RECORD, 53(2), 68–74. https://doi.org/10.1145/3685980.3685998
• NPCS: Native Provenance Computation for SPARQL / Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). NPCS: Native Provenance Computation for SPARQL. In WWW ’24: Proceedings of the ACM Web Conference 2024 (pp. 2085–2093). ACM. https://doi.org/10.1145/3589334.3645557
  Download: NPCS: Native Provenance Computation for SPARQL (1.92 MB)
• Semantic Web: Past, Present, and Future / Scherp, A., Groener, G., Skoda, P., Hose, K., & Vidal, M.-E. (2024). Semantic Web: Past, Present, and Future. Transactions on Graph Data and Knowledge, 2(1), 3:1-3:37. https://doi.org/10.4230/TGDK.2.1.3
  Download: Semantic Web: Past, Present, and Future (1.52 MB)
• Nanomotif: Identification and Exploitation of DNA Methylation Motifs in Metagenomes using Oxford Nanopore Sequencing / Heidelbach, S., Mølvang Dall, S., Bøjer, J. S., Nissen, J., van der Maas, L. N. L., Sereika, M., Kirkegaard, R. H., Jensen, S. I., Kousgaard, S. J., Thorlacius-Ussing, O., Hose, K., Nielsen, T. D., & Albertsen, M. (2024). Nanomotif: Identification and Exploitation of DNA Methylation Motifs in Metagenomes using Oxford Nanopore Sequencing. bioRxiv. https://doi.org/10.34726/8211
  Download: Nanomotif: Identification and Exploitation of DNA Methylation Motifs in Metagenomes using Oxford Nanopore Sequencing (10.9 MB)
• Utilizing Phonetic Similarity for Cross-source and Cross-language Toponym Matching - a Benchmark and Prototype / Sagi, T., Zaga, M., Rusinek, S., Fekete, M. R., Bjerva, J., & Hose, K. (2024). Utilizing Phonetic Similarity for Cross-source and Cross-language Toponym Matching - a Benchmark and Prototype. Research Square (United States). https://doi.org/10.34726/7539
  Download: Utilizing Phonetic Similarity for Cross-source and Cross-language Toponym Matching - a Benchmark and Prototype (505 KB)
• Multimodal representation learning for medical analytics - a systematic literature review / Hansen, E. R., Tomer Sagi, & Hose, K. (2024). Multimodal representation learning for medical analytics - a systematic literature review. Health Informatics Journal, 30(4). https://doi.org/10.1177/14604582241290474
  Download: Multimodal representation learning for medical analytics - a systematic literature review (2.11 MB)
• A benchmark and a multi-stage pipeline for classifying underwater videos at scale / Larsen, S. K., Rasmussen, L. E., Jasulaitis, D., Tomer Sagi, Hose, K., & Lehahn, Y. (2024). A benchmark and a multi-stage pipeline for classifying underwater videos at scale. International Journal of Image and Data Fusion. https://doi.org/10.1080/19479832.2024.2416227
• Proceedings 26th International Conference on Extending Database Technology (EDBT) / Stoyanovich, J., Teubner, J., Mamoulis, N., Pitoura, E., Mühlig, J., Hose, K., Bhowmick, S. S., & Lissandrini, M. (Eds.). (2024). Proceedings 26th International Conference on Extending Database Technology (EDBT) (Vol. 26). OpenProceedings. http://hdl.handle.net/20.500.12708/211105
• Integrating Multi-Modal Spatial Data using Knowledge Graphs – a Case Study of Microflora Danica / Corfixen, M., Heede, T., Sagi, T., Albertsen, M., Nielsen, T. D., & Hose, K. (2024). Integrating Multi-Modal Spatial Data using Knowledge Graphs – a Case Study of Microflora Danica. In A. Hasnain, A. C. Morales Tirado, M. Dumontier, & D. Rebholz-Schuhmann (Eds.), Proceedings of the 7th Workshop on Semantic Web solutions for large-scale biomedical data analytics co-located with The ESWC 2024: Extended Semantic Web Conference (ESWC 2024). https://doi.org/10.34726/8099
  Download: Integrating Multi-Modal Spatial Data using Knowledge Graphs – a Case Study of Microflora Danica (3.19 MB)
• Mining Validating Shapes for Large Knowledge Graphs via Dynamic Reservoir Sampling / Lissandrini, M., Rabbani, K., & Hose, K. (2024). Mining Validating Shapes for Large Knowledge Graphs via Dynamic Reservoir Sampling. In M. Atzori, P. CIACCIA, M. Ceci, F. Mandreoli, D. Malerba, & M. SANGUINETTI (Eds.), Proceedings of the 32nd Symposium on Advanced Database Systems (SEBD 2024) (pp. 25–34). https://doi.org/10.34726/8213
  Download: Mining Validating Shapes for Large Knowledge Graphs via Dynamic Reservoir Sampling (1.52 MB)
• Hypergraphs with Attention on Reviews for Explainable Recommendation / Jendal, T. E., Le, T.-H., Lauw, H. W., Lissandrini, M., Dolog, P., & Hose, K. (2024). Hypergraphs with Attention on Reviews for Explainable Recommendation. In Advances in Information Retrieval (pp. 230–246). Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_14
• KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science / Helali, M., Monjazeb, N., Vashisth, S., Carrier, P., Helal, A., Cavalcante, A., Ammar, K., Hose, K., & Mansour, E. (2024). KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 179–192). IEEE. https://doi.org/10.1109/ICDE60146.2024.00021
• moduli: A Disaggregated Data Management Architecture for Data-Intensive Workflows / Ceravolo, P., Catarci, T., Console, M., Cudre-Mauroux, P., Groppe, S., Hose, K., Pokorný, J., Romero, O., & Wrembel, R. (2024). moduli: A Disaggregated Data Management Architecture for Data-Intensive Workflows. ACM SIGWEB Newsletter : The Newsletter of ACM’s Special Interest Group on Hypertext and Hypermedia, 2024(Winter), 1–16. https://doi.org/10.1145/3643603.3643607
• TARGET: A Major European Project Aiming to Advance the Personalised Management of Atrial Fibrillation-Related Stroke via the Development of Health Virtual Twins Technology and Artificial Intelligence / Ortega-Martorell, S., Olier, I., Ohlsson, M., Lip, G., Hose, K., Tomaszuk, D., & TARGET Consortium. (2024). TARGET: A Major European Project Aiming to Advance the Personalised Management of Atrial Fibrillation-Related Stroke via the Development of Health Virtual Twins Technology and Artificial Intelligence. Thrombosis and Haemostasis, 125(1), 7–11. https://doi.org/10.1055/a-2438-5671
  Project: TARGET (2024–2028)
• A Large Scale Test Corpus for Semantic Table Search / Leventidis, A., Christensen, M. P., Lissandrini, M., Di Rocco, L., Hose, K., & Miller, R. J. (2024). A Large Scale Test Corpus for Semantic Table Search. In SIGIR ’24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1142–1151). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657877
  Download: A Large Scale Test Corpus for Semantic Table Search (1.23 MB)
• Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation / Ortega-Martorell, S., Olier-Caparroso, I., Ohlsson, M., Lip, G., Hose, K., Tomaszuk, D., & TARGET Consortium. (2024). Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation. Trends in Cardiovascular Medicine. https://doi.org/10.1016/j.tcm.2024.12.003
  Project: TARGET (2024–2028)

2023

• How does knowledge evolve in open knowledge graphs? / Polleres, A., Pernisch, R., Bonifati, A., Dell’Aglio, D., Dobriy, D., Dumbrava, S., Etcheverry, L., Ferranti, N., Hose, K., Jiménez-Ruiz, E., Lissandrini, M., Scherp, A., Tommasini, R., & Wachs, J. (2023). How does knowledge evolve in open knowledge graphs? Transactions on Graph Data and Knowledge, 1(1), 11:1-11:59. https://doi.org/10.4230/TGDK.1.1.11
  Download: PDF (4.16 MB)
• Optimizing SPARQL queries over decentralized knowledge graphs / Aebeloe, C., Montoya, G., & Hose, K. (2023). Optimizing SPARQL queries over decentralized knowledge graphs. Semantic Web, 14(6), 1121–1165. https://doi.org/10.3233/SW-233438
  Download: PDF (2.07 MB)
• Tunable Query Optimizer for Web APIs and User Preferences / Zeimetz, T., Hose, K., & Schenkel, R. (2023). Tunable Query Optimizer for Web APIs and User Preferences. In B. Venable, D. Garijoa, & B. Jalaian (Eds.), Proceedings of the 12th Knowledge Capture Conference 2023 (pp. 92–100). Association for Computing Machinery (ACM). https://doi.org/10.1145/3587259.3627542
• StarBench: Benchmarking RDF-star Triplestores / Abouda, G., Aebeloe, C., Dell’Aglio, D., Keen, A., & Hose, K. (2023). StarBench: Benchmarking RDF-star Triplestores. In M. U. Saleem, A.-C. Ngonga Ngomo, D. Graux, F. Orlandi, E. Niazmand, G. Ydler, & M.-E. Vidal (Eds.), Joint Proceedings of the QuWeDa and MEPDaW 2023: 7th Workshop on Storing, Querying and Benchmarking Knowledge Graphs and 9th Workshop on Managing the Evolution and Preservation of the Data Web (QuWeDa-MEPDaW 2023) (pp. 34–49). CEUR-WS.org. https://doi.org/10.34726/5399
  Download: PDF (1.05 MB)
• GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs / Jendal, T., Lissandrini, M., Dolog, P., & Hose, K. (2023). GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs. In V. W. Anelli, P. Basile, G. De Melo, F. Donini, A. Ferrara, C. Musto, F. Narducci, A. Ragone, & M. Zanker (Eds.), Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 17th ACM Conference on Recommender Systems (RecSys 2023) (pp. 80–89). CEUR-WS.org. https://doi.org/10.34726/5395
  Download: PDF (557 KB)
• GLENDA: Querying RDF Archives with Full SPARQL / Pelgrin, O., Taelman, R., Galárraga, L., & Hose, K. (2023). GLENDA: Querying RDF Archives with Full SPARQL. In The Semantic Web: ESWC 2023 Satellite Events (pp. 75–80). Springer. https://doi.org/10.34726/5411
  Download: PDF (502 KB)
• Metagenomic Binning using Connectivity-constrained Variational Autoencoders / Lamurias, A., Tibo, A., Hose, K., Albertsen, M., & Nielsen, T. D. (2023). Metagenomic Binning using Connectivity-constrained Variational Autoencoders. In Proceedings of the 40th International Conference on Machine Learning. 40th International Conference on Machine Learning (ICML 2023), Honolulu, United States of America (the).
• Patient Event Sequences for Predicting Hospitalization Length of Stay / Hansen, E. R., Nielsen, T. D., Mulvad, T., Strausholm, M. N., Sagi, T., & Hose, K. (2023). Patient Event Sequences for Predicting Hospitalization Length of Stay. In J. M. Juarez, M. Marcos, G. Stiglic, & A. Tucker (Eds.), Artificial Intelligence in Medicine : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (pp. 51–56). Springer. https://doi.org/10.1007/978-3-031-34344-5_7
• SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes / Rabbani, K., Lissandrini, M., & Hose, K. (2023). SHACTOR: Improving the Quality of Large-Scale Knowledge Graphs with Validating Shapes. In Companion of the 2023 International Conference on Management of Data (pp. 151–154). https://doi.org/10.1145/3555041.3589723
• Recommending tasks based on search queries and missions / Garigliotti, D., Balog, K., Hose, K., & Bjerva, J. (2023). Recommending tasks based on search queries and missions. Natural Language Engineering, 1–25. https://doi.org/10.1017/S1351324923000219
• Scientific Data Extraction from Oceanographic Papers / Veyhe, B. E., Sagi, T., & Hose, K. (2023). Scientific Data Extraction from Oceanographic Papers. In Y. Ding, J. Tang, J. Sequeda, C. Castillo, & G.-J. Houben (Eds.), WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 800–804). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587595
• Do bridges dream of water pollutants? Towards DreamsKG, a knowledge graph to make digital access for sustainable environmental assessment come true / Garigliotti, D., Bjerva, J., Nielsen, F., Butzbach, A., Lyhne, I., Kørnøv, L., & Hose, K. (2023). Do bridges dream of water pollutants? Towards DreamsKG, a knowledge graph to make digital access for sustainable environmental assessment come true. In Y. Ding, J. Tang, & J. Sequeda (Eds.), WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 724–730). ACM. https://doi.org/10.1145/3543873.3587590
• Visualizing How-Provenance Explanations for SPARQL Queries / Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 212–216). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587350
• Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus / Zaitoun, A., Sagi, T., & Hose, K. (2023). Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus. In Y. Ding, J. Tang, & J. Sequeda (Eds.), WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 1127–1137). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587617
  Download: PDF (948 KB)
• OntoEval: an Automated Ontology Evaluation System / Antonio Zaitoun, Tomer Sagi, & Katja Hose. (2023). OntoEval: an Automated Ontology Evaluation System. In Y. Ding, J. Tang, & J. Sequeda (Eds.), WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 82–85). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587318
• The Need for Better RDF Archiving Benchmarks / Pelgrin, O., Taelman, R., Galárraga, L., & Hose, K. (2023). The Need for Better RDF Archiving Benchmarks. In M. Saleem, A.-C. Ngonga Ngomo, D. Graux, F. Orlandi, E. Niazmand, G. Ydler, & M.-E. Vidal (Eds.), Joint Proceedings of the QuWeDa and MEPDaW 2023: 7th Workshop on Storing, Querying and Benchmarking Knowledge Graphs and 9th Workshop on Managing the Evolution and Preservation of the Data Web (QuWeDa-MEPDaW 2023) (pp. 50–54). https://doi.org/10.34726/5398
  Download: Paper (1010 KB)
• Environmental impact assessment reports in Wikidata and a Wikibase / Nielsen, F. Å., Lyhne, I., Garigliotti, D., Butzbach, A., Ravn Boess, E., Hose, K., & Kørnøv, L. (2023). Environmental impact assessment reports in Wikidata and a Wikibase. In Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with 20th European Semantic Web Conference (ESWC 2023) (pp. 1–8). CEUR-WS.org. https://doi.org/10.34726/5421
  Download: PDF (1.53 MB)
• Graph Neural Networks for Metagenomic Binning / Lamurias, A., Tibo, A., Hose, K., Albertsen, M., & Nielsen, T. D. (2023). Graph Neural Networks for Metagenomic Binning. In The 2023 ICML Workshop on Computational Biology. Accepted Submissions. 40th International Conference on Machine Learning (ICML 2023), Honolulu, United States of America (the). ICML compbio workshop. https://doi.org/10.34726/5406
  Download: PDF (441 KB)
• Extraction of validating shapes from very large knowledge graphs / Rabbani, K., Lissandrini, M., & Hose, K. (2023). Extraction of validating shapes from very large knowledge graphs. Proceedings of the VLDB Endowment, 16(5), 1023–1032. https://doi.org/10.14778/3579075.3579078
  Download: Paper (1.84 MB)

2018

• Advances in Database Technology — EDBT 2018 / Böhlen, M., Pichler, R., May, N., Rahm, E., Wu, S.-H., & Hose, K. (Eds.). (2018). Advances in Database Technology — EDBT 2018. EDBT 2018. https://doi.org/10.5441/002/edbt.2018.01

• SHACL shapes extraction for evolving knowledge graphs / Pürmayr, E. (2025). SHACL shapes extraction for evolving knowledge graphs [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.120502
  Download: PDF (2.24 MB)

• Distinguished Meta-Reviewer Award
  2025 / EDBT/ICDT 2025 Joint Conference / Spain / Website
• Manfred Paul Award - Extraction of Validating Shapes from very large Knowledge Graphs (VLDB 2023)
  2024 / International Federation for Information Processing (IFIP) / Germany / Website
• Best Demo Award - GLENDA: Querying over RDF Archives with SPARQL
  2023 / European Semantic Web Conference (ESWC) 2023 / Greece / Website
• Best Paper Award - Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus
  2023 / International Workshop on Natural Language Processing for Knowledge Graph Creation (NLP4KGC) 2023 / USA / Website

Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.

Until then, please visit Katja Hose’s research profile in TISS .