Data Mining, Validation, and Collaborative Knowledge Capture

Author:

Atzmueller Martin1,Beer Stephanie2,Puppe Frank3

Affiliation:

1. University of Kassel, Germany

2. University Clinic of Wuerzburg, Germany

3. University of Wuerzburg, Germany

Abstract

For large-scale data mining, utilizing data from ubiquitous and mixed-structured data sources, the extraction and integration into a comprehensive data-warehouse is usually of prime importance. Then, appropriate methods for validation and potential refinement are essential. This chapter describes an approach for integrating data mining, information extraction, and validation with collaborative knowledge management and capture in order to improve the data acquisition processes. For collaboration, a semantic wiki-enabled system for knowledge and experience management is presented. The proposed approach applies information extraction techniques together with pattern mining methods for initial data validation and is applicable for heterogeneous sources, i.e., capable of integrating structured and unstructured data. The methods are integrated into an incremental process providing for continuous validation options. The approach has been developed in a health informatics context: The results of a medical application demonstrate that pattern mining and the applied rule-based information extraction methods are well suited for discovering, extracting and validating clinically relevant knowledge, as well as the applicability of the knowledge capture approach. The chapter presents experiences using a case-study in the medical domain of sonography.

Publisher

IGI Global

Reference25 articles.

1. Data mining;M.Atzmueller;Applied natural language processing and content analysis: Advances in identification, investigation and resolution,2011

2. Atzmueller, M., Beer, S., & Puppe, F. (2009). A data warehouse-based approach for quality management, evaluation and analysis of intelligent systems using subgroup mining. In Proceedings of 22nd International Florida Artificial Intelligence Research Society Conference, (pp. 372–377). AAAI Press.

3. Atzmueller, M., Haupt, F., Beer, S., & Puppe, F. (2009). Knowta: Wiki-enabled social tagging for collaborative knowledge and experience management. Proceedings International Workshop on Design, Evaluation, and Refinement of Intelligent Systems, Krakow, Poland

4. Atzmueller, M., Kluegl, P., & Puppe, F. (2008). Rule-based information extraction for structured data acquisition using TextMarker. In Proceedings of LWA 2008 (Knowledge Discovery and Machine Learning Track), University of Wuerzburg

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3