Content collection for the labelling of health-related web content

被引:0
|
作者
Stamatakis, K. [1 ]
Metsis, V. [1 ]
Karkaletsis, V. [1 ]
Ruzicka, M. [2 ]
Svatek, V. [2 ]
Amigo, E. [3 ]
Polla, M. [4 ]
Spyropoulos, C. [1 ]
机构
[1] Natl Ctr Sci Res Demokritos, Prague, Czech Republic
[2] Univ Econ, Prague, Czech Republic
[3] Univ Nacl Educ Distancia, Madrid, Spain
[4] Aalto Univ, FIN-02150 Espoo, Finland
来源
ARTIFICIAL INTELLIGENCE IN MEDICINE, PROCEEDINGS | 2007年 / 4594卷
关键词
content labelling; health information quality; web content collection; focused crawling; spidering; content classification; machine learning;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the number of health-related web sites in various languages increases, so does the need for control mechanisms that give the users adequate guarantee on whether the web resources they are visiting meet a minimum level of quality standards. Based upon state-of-the-art technology in the areas of semantic web, content analysis and quality labelling, the MedIEQ project, integrates existing technologies and tests them in a novel application: the automation of the labelling process in health-related web content. MedIEQ provides tools that crawl the web to locate unlabelled health web resources, to label them according to pre-defined labelling criteria, as well as to monitor them. This paper focuses on content collection and discusses our experiments in the English language.
引用
收藏
页码:341 / 345
页数:5
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