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
Unfortunately, no list of projects can be displayed here at the moment. Until the list is available again, the project search on the ZHAW homepage can be used.
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Deriu, Jan Milan; Cieliebak, Mark,
2018.
Syntactic manipulation for generating more diverse and interesting texts[paper].
In:
Proceedings of the 11th International Conference on Natural Language Generation.
11th International Conference on Natural Language Generation (INLG 2018), Tilburg, The Netherlands, 5-8 November 2018.
Association for Computational Linguistics.
pp. 22-34.
Available from: https://doi.org/10.18653/v1/W18-6503
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Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo,
2018.
Trace and detect adversarial attacks on CNNs using feature response maps[paper].
In:
Artificial Neural Networks in Pattern Recognition.
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
Springer.
pp. 346-358.
Lecture Notes in Computer Science ; 11081.
Available from: https://doi.org/10.1007/978-3-319-99978-4_27
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Imhof, Melanie; Braschler, Martin,
2017.
A study of untrained models for multimodal information retrieval.
Information Retrieval Journal.
21(1), pp. 81-106.
Available from: https://doi.org/10.1007/s10791-017-9322-x
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Cieliebak, Mark; Deriu, Jan Milan; Egger, Dominic; Uzdilli, Fatih,
2017.
A Twitter corpus and benchmark resources for german sentiment analysis[paper].
In:
5th International Workshop on Natural Language Processing for Social Media, Boston MA, USA, 11 December 2017.
Association for Computational Linguistics.
pp. 45-51.
Available from: https://doi.org/10.18653/v1/W17-1106
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Kodiyan, Don; Hardegger, Florin; Neuhaus, Stephan; Cieliebak, Mark,
2017.
Author profiling with bidirectional RNNs using attention with GRUs : notebook for PAN at CLEF 2017[paper].
In:
CLEF 2017 Evaluation Labs and Workshop – Working Notes Papers.
CLEF 2017 Conference and Labs of the Evaluation Forum, Dublin, Ireland, 11-14 September 2017.
RWTH Aachen.
Available from: https://doi.org/10.21256/zhaw-1531