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Katja Hose

Univ.Prof. Dr.-Ing.

Research Focus

  • Information Systems Engineering: 30%
  • Logic and Computation: 70%

Research Areas

  • Information and Knowledge Engineering, Ontologies, Data Engineering, Web data extraction and integration, Information Integration, Knowledge Management, Semantic Web, Data Quality, Distributed database systems, Data Warehousing, Data Integration, Linked Data, Open Data, Knowledge-Based Systems, Web-based Databases, Knowledge Graphs, Knowledge Graph Management Systems, Data Management, Artificial Intelligence, Data Extraction and Integration, Big Data, Database Systems, Databases, Scalable Systems
Katja Hose

About

My current research is rooted in data management and knowledge engineering and and spans theory, algorithms, and applications of Data Science including graph databases, knowledge graphs, querying, analytics, and machine learning.

In the past couple of years, I have gained extensive experience in interdisciplinary data science in collaborations with colleagues from bioscience, medicine, and environmental assessment.

Roles

  • Full Professor
    Databases and Artificial Intelligence, E192-02
  • Curriculum Coordinator
    Master / Area / Data Management
  • Faculty Council
    Principal Member
  • Curriculum Commission for Informatics
    Substitute Member

Contact

2024W

2025S

 

2025

2024

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

 

  • 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 .