Abstract
The real-life customer servicing databases share a useful characteristic which can be properly exploited to solve a common problem with the databases themselves. This useful characteristic is that either remark or memo fields are always included in the databases; customer service representatives can use these fields to write down specific things about the service records. This design helps alleviate the following common difficulty with the categorization of customer-service-related problems: customer requested service records are often misclassified owing to human ignorance or bad design of problem categorization. In this paper we propose an ontology-supported technique to preprocess the remark fields, trying to discover meaningful information to help re-categorize misclassified service records. This process restores the database into one with more meaningful data in each record, which facilitates the mining of better association rules. The technique was applied to a real-life trouble shooting database obtained from a telecommunication company. The results show a substantial improvement in the quality of mined trouble shooting rules can be obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Noy, N.F., McGuinness, D.L.: Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880 (March 2001)
Tsai, C.H.: MMSEG: A Word Identification System for Mandarin Chinese Text Based on Two Variants of The Maximum Matching Alforithm (1996), Availible at http://www.geocities.com/hao510/mmseg/
Joachims, T.: A Probabilistic Analysis of the Rocchio Algrithm with TFIDF for Text Careagorization. Technical Report of CMU-CS-96-118, Department of Computer Science, Carnegie Mellon University, Pennsylvania, USA (March 1996)
Neto, J.L., Santos, A., Kaestner, C., Freitas, A.: Document Clustering and Text Summarization. In: Proceedings of the Fourth International Conference Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), London, January 2000, pp. 41–55 (2000)
Zou, Q., Chu, W., David, J., Chiu, H.: A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns. Knowledge and Information Systems 4, 466–482 (2002)
Liao, B.C.: An Intelligent Proxy Agent for FAQ Service. Master Thesis, Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chu, BH., Liao, IK., Ho, CS. (2005). An Ontology-Supported Database Refurbishing Technique and Its Application in Mining GSM Trouble Shooting Rules. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_140
Download citation
DOI: https://doi.org/10.1007/11553939_140
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
eBook Packages: Computer ScienceComputer Science (R0)