Intelligent Information Systems
We Derive Value from Data and Information
- How to leverage information?
- How to find new topics and trends?
- How to derive insight from heterogeneous/unstructured data and information?
- How to allow a «natural» access to data?
- How can software link data automatically?
These are but a few of the questions that the Intelligent Information Systems (IIS) group of the InIT is working to answer. While the “data and information flood” is often discussed negatively, we see a great opportunity to leverage data and information using the right approaches – both at search-time, as well as during analysis.
The research group transfers insights derived from research and development into teaching for students of the computer science curricula. It offers modules such as “Information Engineering 1 (Information Retrieval)”, “Information Engineering 2 (Data Warehousing & Big Data)” and "Databases". The group is active in both national and international research projects of the EU framework programs.
Research Topics
The Intelligent Information Systems group develops solutions for a changing, data-driven world. It performs research at the intersection of databases (DB), information retrieval (IR), data engineering (DE), natural language processing (NLP) and machine learning (ML)
The group covers two main research lines:
Big Data and Nano Data
We solve challenging problems when working with a range of datasets from very small (nano data) to very large (big data), where the nature of the problems change drastically as we work on different scales:
Current research:
- Information retrieval for small document collections
- Machine learning for query optimization
- Artificial intelligence for data integration and cleaning
- Quantum databases and quantum machine learning
Data Understanding
As we strive for "intelligent" solutions to data-driven problems, classical information systems need to process data at a different level, interpreting it to gain important information. Both structured and unstructured data must be processed not on a mechanical, but on a semantic level - e.g. by using natural language processing and understanding. Data is ultimately connected through graph structures or made accessible via semantic search.
Current research:
- Natural language interfaces for databases
- Semantic search on entities
- Knowledge graph construction
- Question answering over knowledge graphs
- Stream analytics and event detection
- Information retrieval evaluation
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Garrido-Hidalgo, Celia; Fürst, Jonathan; Roda-Sanchez, Luis; Olivares, Teresa; Fernández-Caballero, Antonio,
2023.
Lessons learned on the design of a predictive agent for LoRaWAN network planning [paper].
In:
Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : the PAAMS Collection.
21st International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), Guimarães, Portugal, 12th-14th July 2023.
Cham:
Springer.
Lecture Notes in Computer Science ; 13955.
Available from: https://doi.org/10.1007/978-3-031-37616-0_8
-
Mildenberger, Thoralf; Braschler, Martin; Ruckstuhl, Andreas; Vorburger, Robert; Stockinger, Kurt,
2023.
The role of data scientists in modern enterprises : experience from data science education.
SIGMOD Record.
52(2), pp. 48-52.
Available from: https://doi.org/10.21256/zhaw-27357
-
Monteiro Simoes, Ricardo Daniel; Huber, Patrick; Meier, Nicola; Smailov, Nikita; Füchslin, Rudolf M.; Stockinger, Kurt,
2023.
Experimental evaluation of quantum machine learning algorithms.
IEEE Access.
11, pp. 6197-6208.
Available from: https://doi.org/10.1109/ACCESS.2023.3236409
-
Fürst, Jonathan; Fadel Argerich, Mauricio; Cheng, Bin,
2023.
VersaMatch : ontology matching with weak supervision [paper].
In:
Koutrika, Georgia; Yang, Jun, eds.,
Proceedings of the VLDB Endowment.
49th Conference on Very Large Data Bases (VLDB), Vancouver, Canada, 28 August - 1 September 2023.
Association for Computing Machinery.
pp. 1305-1318.
Available from: https://doi.org/10.14778/3583140.3583148
-
von Däniken, Pius; Deriu, Jan Milan; Agirre, Eneko; Brunner, Ursin; Cieliebak, Mark; Stockinger, Kurt,
2022.
Improving NL-to-Query systems through re-ranking of semantic hypothesis [paper].
In:
Abbas, Mourad; Freihat, Abed Alhakim, eds.,
Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022).
5th International Conference on Natural Language and Speech Processing (ICNLSP), online, 16-17 December 2022.
Association for Computational Linguistics.
pp. 57-67.
Available from: https://doi.org/10.21256/zhaw-26147