Literature DB >> 21712354

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources.

Miguel A Mayer1, Pythagoras Karampiperis, Antonis Kukurikos, Vangelis Karkaletsis, Kostas Stamatakis, Dagmar Villarroel, Angela Leis.   

Abstract

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various "trust marks" and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.

Entities:  

Mesh:

Year:  2011        PMID: 21712354     DOI: 10.1177/1460458211405004

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  3 in total

1.  Why decision support systems are important for medical education.

Authors:  Stathis Th Konstantinidis; Panagiotis D Bamidis
Journal:  Healthc Technol Lett       Date:  2016-03-21

2.  HealthTrust: a social network approach for retrieving online health videos.

Authors:  Luis Fernandez-Luque; Randi Karlsen; Genevieve B Melton
Journal:  J Med Internet Res       Date:  2012-01-31       Impact factor: 5.428

3.  Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

Authors:  Nadine Tix; Paul Gießler; Ursula Ohnesorge-Radtke; Cord Spreckelsen
Journal:  JMIR Med Educ       Date:  2015-11-11
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.