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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Braschler, Martin; Di Nunzio, Giorgio Maria; Ferro, Nicola; Peters, Carol,
2005.
CLEF 2004 : ad hoc track overview and results analysis[paper].
In:
Multilingual Information Access for Text, Speech and Images.
5th Workshop of the Cross-Language Evaluation Forum (CLEF 2004) Bath, United Kingdom, 15-17 September 2004.
Heidelberg:
Springer.
pp. 10-26.
Lecture Notes in Computer Science ; 3491.
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Schäuble, Peter; Braschler, Martin,
2005.
Portalspezifische Informationssuche für den Kanton Zürich.
Datenbank-Spektrum.
2005(12), pp. 26-30.
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Braschler, Martin,
2004.
CLEF 2003 : overview of results[paper].
In:
Comparative Evaluation of Multilingual Information Access Systems.
4th Workshop of the Cross-Language Evaluation Forum (CLEF 2003), Trondheim, Norway, 21-22 August 2003.
Heidelberg:
Springer.
pp. 44-63.
Lecture Notes in Computer Science ; 3237.
-
Braschler, Martin; Peters, Carol,
2004.
CLEF 2003 methodology and metrics[paper].
In:
Comparative Evaluation of Multilingual Information Access Systems.
4th Workshop of the Cross-Language Evaluation Forum (CLEF 2003), Trondheim, Norway, 21-22 August 2003.
Heidelberg:
Springer.
pp. 7-20.
Lecture Notes in Computer Science ; 3237.
-
Braschler, Martin,
2004.
Combination approaches for multilingual text retrieval.
Information Retrieval Journal.
7(1/2), pp. 183-204.
Available from: https://doi.org/10.21256/zhaw-1833