Dealing with Background Knowledge in the SEWEBAR Project

SEWEBAR is a research project the goal of which is to study possibilities of dissemination of analytical reports through Semantic Web. We are interested in analytical reports presenting results of data mining. Each analytical report gives answer to one analytical question. Lot of interesting analytical questions can be answered by GUHA procedures implemented in the LISp-Miner system. The SEWEBAR project deals with these analytical questions. However the process of formulating and answering such analytical questions requires various background knowledge. The paper presents first steps in storing and application of several forms of background knowledge in the SEWEBAR project. Examples concerning dealing with medical knowledge are presented.

[1]  Jan Rauch,et al.  Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations , 2007 .

[2]  Jan Rauch,et al.  GUHA method and granular computing , 2005, 2005 IEEE International Conference on Granular Computing.

[3]  Jan Rauch,et al.  Ontology-Enhanced Association Mining , 2005, EWMF/KDO.

[4]  Jan Rauch,et al.  Mining for Patterns Based on Contingency Tables by KL-Miner - First Experience , 2006, Foundations and Novel Approaches in Data Mining.

[5]  Jan Rauch,et al.  An Alternative Approach to Mining Association Rules , 2005, Foundations of Data Mining and knowledge Discovery.

[6]  Jan Rauch,et al.  Mining and Querying in Association Rule Discovery , 2002, International Workshop on Knowledge Discovery in Inductive Databases.

[7]  Tsau Young Lin,et al.  Foundations and Novel Approaches in Data Mining , 2006, Studies in Computational Intelligence.

[8]  Petra Perner,et al.  Advances in Data Mining , 2002, Lecture Notes in Computer Science.

[9]  Jan Rauch,et al.  Reporting Data Mining Results in a Natural Language , 2005, Foundations of Data Mining and knowledge Discovery.

[10]  Dunja Mladenic,et al.  Semantics, Web and Mining , 2008 .

[11]  Cindy Farquhar,et al.  3 The Cochrane Library , 1996 .

[12]  Jan Rauch,et al.  Content-based Retrieval of Analytical Reports , 2002, RuleML.

[13]  Jan Rauch.  Logical Calculi for Knowledge Discovery in Databases , 1997, PKDD.

[14]  T. Havránek,et al.  Mechanizing Hypothesis Formation: Mathematical Foundations for a General Theory , 1978 .

[15]  Samik Basu,et al.  Local and On-the-fly Choreography-based Web Service Composition , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[16]  Jan Rauch,et al.  Mining for 4ft Association Rules , 2000, Discovery Science.

[17]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[18]  Jan Rauch.  Project SEWEBAR Considerations on Semantic Web and Data Mining , 2007, IICAI.

[19]  Tsau Young Lin,et al.  Foundations of Data Mining and knowledge Discovery , 2005, Studies in Computational Intelligence.

[20]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[21]  Martin Ralbovský,et al.  Using Disjunctions in Association Mining , 2007, Industrial Conference on Data Mining.

[22]  Jan Rauch,et al.  Logic of Association Rules , 2004, Applied Intelligence.

[23]  Milan Šimůnek.  Academic KDD Project LiSp-Miner , 2003 .

[24]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[25]  Jan Rauch,et al.  LAREDAM - Considerations on System of Local Analytical Reports from Data Mining , 2008, ISMIS.

[26]  Anthony Hunter,et al.  Elements of Argumentation , 2007, ECSQARU.