Rapid knowledge capture using subgroup discovery with incremental refinement
Pages 31 - 38
Abstract
This paper presents an approach for rapid knowledge capture using subgroup-discovery techniques. The method enables the acquisition of scoring rules - a knowledge representation that is easy to understand and to maintain. Furthermore, the method features an incremental refinement step that can be applied for fine-tuning of the learned relations. We provide a case study demonstrating the applicability of the presented method using a knowledge base from the biological domain.
References
[1]
M. Atzmueller, J. Baumeister, A. Hemsing, E.-J. Richter, and F. Puppe. Subgroup Mining for Interactive Knowledge Refinement. In Proc. 10th Conference on Artificial Intelligence in Medicine, LNAI 3581, pages 453--462, Berlin, 2005. Springer.
[2]
M. Atzmueller and F. Puppe. SD-Map -- A Fast Algorithm for Exhaustive Subgroup Discovery. In Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 6--17, Berlin, 2006. Springer.
[3]
J. Baumeister. Agile Development of Diagnostic Knowledge Systems. IOS Press, DISKI 284, 2004.
[4]
J. Baumeister, J. Bregenzer, and F. Puppe. Grey-Box Robustness Testing of Rule Systems. In Proc. 29th Annual German Conference on Artificial Intelligence, LNAI 4314, pages 346--360. Springer, 2006.
[5]
H.-P. Buscher, C. Engler, A. Fuhrer, S. Kirschke, and F. Puppe. HepatoConsult: A Knowledge-Based Second Opinion and Documentation System. Artificial Intelligence in Medicine, 24(3):205--216, 2002.
[6]
H.-P. Eich and C. Ohmann. Internet-Based Decision-Support Server for Acute Abdominal Pain. Artificial Intelligence in Medicine, 20(1):23--36, 2000.
[7]
R. Ernst. Untersuchung verschiedener Problemlösungsmethoden in einem Experten- und Tutorsystem zur makroskopischen Bestimmung krautiger Blütenpflanzen {Analysis of problem solving methods with an expert and tutoring system for the macroscopic classification of flowers}. Master's thesis, University Würzburg, Biology department, 1996.
[8]
A. Ginsberg. Automatic Refinement of Expert System Knowledge Bases. Morgan Kaufmann Publisher, 1988.
[9]
N. Lavrac, B. Kavsek, P. Flach, and L. Todorovski. Subgroup Discovery with CN2-SD. Journal of Machine Learning Research, 5:153--188, 2004.
[10]
R. A. Miller, H. E. Pople, and J. Myers. INTERNIST-1, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine. NEJM, 307:468--476, 1982.
[11]
M. Neumann, J. Baumeister, M. Liess, and R. Schulz. An Expert System to Estimate the Pesticide Contamination of Small Streams using Benthic Macroinvertebrates as Bioindicators, Part 2: The Knowledge Base of LIMPACT. Ecological Indicators, Elsevier Science, 2(4):391--401, 2003.
[12]
C. Ohmann and et al. Clinical Benefit of a Diagnostic Score for Appendicitis: Results of a Prospective Interventional Study. Archives of Surgery, 134:993--996, 1999.
[13]
F. Puppe. Knowledge Reuse among Diagnostic Problem-Solving Methods in the Shell-Kit D3. International Journal of Human-Computer Studies, 49:627--649, 1998.
[14]
F. Puppe. Knowledge Formalization Patterns. In Proceedings of PKAW 2000, Sydney, Australia, 2000.
[15]
I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edition edition, 2000.
[16]
S. Wrobel. An Algorithm for Multi-Relational Discovery of Subgroups. In Proc. 1st European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-97), pages 78--87, Berlin, 1997. Springer.
Index Terms
- Rapid knowledge capture using subgroup discovery with incremental refinement
Recommendations
Optimization of knowledge discovery process using domain knowledge
IIS '97: Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)Modern database technologies process large volumes of data to discover new knowledge. Some large databases make discovery computationally expensive. Additional knowledge, known as domain or background knowledge, can often guide and restrict the search ...
Multi-level contextual product development knowledge management in PLM
Product Lifecycle Management (PLM) manages product data and processes and generally forms a huge product knowledge base, but how to leverage it for later product development is not well emphasised and discussed. Based on the practices of PLM ...
Using domain knowledge in knowledge discovery
CIKM '99: Proceedings of the eighth international conference on Information and knowledge managementWith the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to focus search to a potentially interesting ...
Comments
Information & Contributors
Information
Published In
Copyright © 2007 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 October 2007
Check for updates
Author Tags
Qualifiers
- Article
Conference
K-CAP07: International Conference on Knowledge Capture 2007
October 28 - 31, 2007
BC, Whistler, Canada
Acceptance Rates
Overall Acceptance Rate 55 of 198 submissions, 28%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 305Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in