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Projects / Programmes source: ARIS

Inteligentna analiza podatkov, računalniška logika in jezikoslovje (Slovene)

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Periods
January 1, 1999 - December 31, 2003
Research activity

Code Science Field Subfield
2.07.00  Engineering sciences and technologies  Computer science and informatics   
2.06.00  Engineering sciences and technologies  Systems and cybernetics   

Code Science Field
P176  Natural sciences and mathematics  Artificial intelligence 
B110  Biomedical sciences  Bioinformatics, medical informatics, biomathematics biometrics 
H350  Humanities  Linguistics 
B003  Biomedical sciences  Ecology 
P175  Natural sciences and mathematics  Informatics, systems theory 
P110  Natural sciences and mathematics  Mathematical logic, set theory, combinatories 
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (7)
no. Code Name and surname Research area Role Period No. of publications
1.  05806  PhD Bojan Cestnik  Computer science and informatics  Researcher  2001 - 2003  196 
2.  11130  PhD Sašo Džeroski  Computer science and informatics  Researcher  2001 - 2003  1,204 
3.  05023  PhD Tomaž Erjavec  Linguistics  Researcher  2001 - 2003  636 
4.  20243  PhD Branko Kavšek  Computer intensive methods and applications  Researcher  2001 - 2003  134 
5.  08949  PhD Nada Lavrač  Computer science and informatics  Head  2001 - 2003  867 
6.  16302  PhD Ljupčo Todorovski  Computer science and informatics  Researcher  2001 - 2003  443 
7.  22279  PhD Bernard Ženko  Computer science and informatics  Researcher  2002 - 2003  172 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,664 
Abstract
The gap between the amount of data gathered and stored in electronic form and the human capability of data analysis and understanding is increasing in all fields of human activity. The goal of the research program is the development and use of methods for intelligent data analysis including Knowledge Discovery in Databases and Data Mining. These methods are being developed in close connection with the developments in the areas of Computational Logic, in particular Inductive Logic Programming, Constraint Logic Programming and Computational Linguistics. The main link between these areas is the need for extensive use of human expert knowledge in problem solving and data analysis, as well as the understandability of used and extracted knowledge, enabled by the use of logical formalisms for knowledge representation. The main developments are expected in the following areas. First, in the area of intelligent data analysis the program’s goal is the development of new and adaptation of existing machine learning and inductive logic programming methods with an emphasis on solving practical problems in the following areas: medical data analysis and synthesis of diagnostic and prognostic rules, analysis of environmental data, analysis of data on media as well as the analysis of commercial and management data. Second, for efficient problem solving and modeling of problems with Constraint Logic Programming it is necessary to incorporate into the model domain knowledge and previous experience in modeling of similar types of problems. This can be achieved in collaboration with experts, whereas the research goal of this program is the automatic synthesis of such knowledge from examples, using machine learning. Improved CLP methods will be used for solving different complex combinatorial problems, including job shop scheduling and schedule generation. Third, in the areas of Computational Linguistics and Language Technologies the main goals will be the gathering and analysis of text data, in particular the acquisition and construction of Slovenian and multi-lingual corpora and lexica, as well as development of models of the Slovenian language, mostly by use of machine learning and inductive logic programming.
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