gms | German Medical Science

53. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

15. bis 18.09.2008, Stuttgart

Information tailoring with RSS

Meeting Abstract

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  • Simon Hölzer - University of Giessen, Giessen, Germany
  • Ralf Schweiger - University of Giessen, Giessen, Germany
  • Joerg Rieger - University of Applied Science, Giessen, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 53. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Stuttgart, 15.-19.09.2008. Düsseldorf: German Medical Science GMS Publishing House; 2008. DocP-52

The electronic version of this article is the complete one and can be found online at:

Published: September 10, 2008

© 2008 Hölzer et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Introduction and Background

The growing volume of information needs to be canalized more efficiently to meet the user requirements today. The biomedical sciences are especially vulnerable in this regard, since they are strongly oriented toward text-based knowledge sources. The technical resources needed to master these tasks are already available in the form of the data standards and tools of the Semantic Web. They include Rich Site Summaries (RSS), which have become established means of distributing and syndicating conventional news messages and blogs. We describe a comprehensive framework to use RSS in information tailoring and filtering.

Methods and Results

The system treats all sorts of content as electronic messages or news items. These messages and news items are then provided in the form of standardized, structured units (in this case RSS 2.0 feeds) for further processing. This approach is based on merging the basic concept of conventional data management, such as relational databases (including content management systems), with processing partially structured (web-based) multimedia documents. Besides this, it enables even inexperienced users to access available information in a more effective and targeted manner than what was previously possible. This provides a basis for development of intelligent applications that incrementally exploit the potential added value of content markup, metadata and semantic linking. The possibility of using this sort of processing and filtering for objects such as headlines, topics, authors and associated original resources is becoming increasingly important for mastering the unrestrained flood of data and information emerging from the anarchic Internet and the deep web. This approach combines compaction of information contents into primary descriptive attributes with transformation into a standardized target format. This allows resources that are fundamentally different with regard to structure and format to be processed (handled) in a uniform manner. As a result, sources whose contents deal with identical or similar topics (e.g. from different editorial entities) can compete with each other with respect to topicality and quality, regardless of how the individual media are distributed. Various sources of information dealing with the same topic can be consulted and directly compared with each other. Such services are of considerable benefit in the medical environment, since rapid, accurate localization of information sources, in particular distributed and dynamically generated information sources, allow the quality of the individual resources to be assessed so they can be integrated into the user’s routine work.

Discussion and Perspectives

Standard interfaces such as RSS allow content provider to easily deliver multimedia content to their targeted audience (end-users, websites and applications). RSS is the most popular form of providing media-specific metadata for data exchange and direct integration. Our RSS filtering technology acts as an aggregator and personalized filter of this pre-formatted content. The system can explore existing RSS metadata and is able to identify automatically relevant keywords and topics from that content. The concept integrates new methods such as „topic indexing“ and „topic converging“ that allow to update and search complex information networks in an efficient and economic way (see also references). The webservice (, German platform merges all these functions with dynamic news and social impact (Web 2.0). The system automatically learns to understand the interests of individual users. Given profiles are matched with news (as one possible source of information) and compared with the readings of like-minded people.


Tim Berners-Lee. Semantic web road map. 1998. Available at: External link
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Robinson J, de Lusignan S, Kostkova P, Madge B, Marsh A, Biniaris C. The Primary Care Electronic Library: RSS feeds using SNOMED-CT indexing for dynamic content delivery. Inform Prim Care. 2006;14(4):247-52.
Hoelzer S, Schweiger RK, Rieger J, Meyer M. Dealing with an information overload of health science data: structured utilisation of libraries, distributed knowledge in databases and Web content. Stud Health Technol Inform. 2006;124:549-54.