Ontology-Assisted Query Formulation in Multidimensional Association Rule Mining 1 Chin-Ang Wu Wen-Yang Lin, Chuan-Chun Wu Department of Information Management, I- Shou University, Kaohsiung County, Taiwan 840, R.O.C. cwu@csu.edu.tw Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan 811, R.O.C. Department of Information Management, I- Shou University, Kaohsiung County, Taiwan 840, R.O.C. miswucc@isu.edu.tw wylin@nuk.edu.tw 1 Chin-Ang Wu is also a lecturer in Cheng Shiu University, Niaosong Township,Kaohsiung County 833.hsiung County Abstract In the information era, the development of various electronic information resources have dramatically grown, mining useful information from large databases has become one of the most important issues in information research for users. Information technologies have provided many applicable solutions, yet there are still many problems that cause users to spend extra time to get real knowledge. In this paper, we show an ontology based system framework for multi-dimensional association rule mining that incorporates ontologies in order to help users develop correct queries, reduce the system resource consumption and improve the efficiency of the mining process. 1. Introduction In the knowledge economics era, the efficient use of data to promote business competition and opportunities is an important movement. Information technologies have provided many applicable solutions, but many decision analyses are still greatly dependent on professionals’ manual judgments. We expect that a good system should provide efficient, convenient and thorough mining mechanisms, as well as meet the users’ professional demands. In other words, developing a system environment that provides users a way to repeatedly discover important rules or facts based on their professional judgment is absolutely an issue that research workers in the knowledge mining fields can not avoid. Currently, the research on the integration of data mining and data warehousing are mostly concentrated on data mining from data cube or multi-dimensional database. J. Han’s research group pioneered this research subject. They combined OLAP and data mining to develop DBMiner, a system that provides minings of association rules, classification, prediction and clustering from data cube[3]. To provide OLAP with efficient data storage and data analysis, star schema, proposed by Kimball [5], is a prevalent data model being used in many data warehouse systems. But the hierarchical and other semantic relationships among attributes which may be helpful to the data mining systems are not able to be displayed in the star schema and hence leave data mining with limitations. Thus an ontology, which is used to describe and represent domain knowledge [4], can be applied to collect the semantic knowledge of data warehouse. The main purpose of this paper is to show an ontology based system framework for multidimensional association rule mining that incorporates ontologies in order to help users develop correct queries and reduce the system resource consumption and improve the efficiency of the mining process. The rest of this paper is organized as follows. In section 2, we introduce multidimensional association rule mining and its query with constriants. The framework of ontology-based multidimensional association rule mining system is explained in section 3. In section 4, query formulation and checking with the help of ontologies are described. And finally this paper concluded in section 5. 2. Multidimensional association rule mining and its query with constraints 2007 IEEE International Conference on Granular Computing 0-7695-3032-X/07 $25.00 © 2007 IEEE DOI 10.1109/GrC.2007.106 358 2007 IEEE International Conference on Granular Computing 0-7695-3032-X/07 $25.00 © 2007 IEEE DOI 10.1109/GrC.2007.106 358 Authorized licensed use limited to: CHENG SHIU UNIVERSITY. Downloaded on August 13,2010 at 05:38:30 UTC from IEEE Xplore. Restrictions apply.