SYNERGIE

Competence Center: Information Retrieval and Machine Learning
Contact: Prof. Dr. Sahin Albayrak
Partners: Prof. Obermayer, NI, TU BerlinProf. Sikora, NÜ, TU BerlinProf. Rammert, ZTG, TU Berlin  

 

Modern media provide many ways for documenting and publishing scientific results. Even today, scientists are facing the problem of finding all information relevant for their area of interest from the numerous sources (e.g. libraries, publishers, web-sites, peers, etc).

The near future will bring even more ways for the communication between the scientists and the distribution of their work.

Existing information portals are static. Scientific communities, however, require dynamic platforms for the efficient access to relevant information, for fast and effective dissemination of new discoveries, as well as for distributed, collaborative work.

The co-operative and interdisciplinary research, which is the core of the e-Science vision, can only be achieved by

  • Integrating existing information sources,
  • Conditioning and enhancing the information with relations and semantic concepts,
  • Providing scientists with a personalized information management system, and
  • Offering a collaborative working environment for scientists with similar interests.

This approach serves both to improve the efficiency of scientific work and to support the life-long learning process of scientists.

Goal

The paradigm of SYNERGIE is to link information and knowledge with the help of innovative information services for the better support of users.

The goal is to provide these innovative services in the form of a knowledge-rich, collaborative platform for the formation and the support of scientific communities.

The integration of different information sources and cooperative services enables a new level of synergies between currently distinct areas of research.

Implementation

The approach of SYNERGIE is to extend a platform for intelligent information search and management with community services. Different kinds of information, e.g. books, publications, proceedings, multimedia content and articles, are collected from different sources with a high reputation for their scientific content, analyzed, (semantically) annotated and filtered according to the users’ information needs. Advanced assistants support the user in defining and refining their information need, managing the retrieved information, as well as finding and contacting peer scientists. Services for the discussion and exchange with other users, foundation of communities, and collaborative work support the scientists in different aspects of their work.

At different stages prototypes will be released. Their acceptance and the actual scientists’ needs will be evaluated by sociologists. The results of these evaluations will be taken into account for the next release.

These evaluations will not provide mere estimations of the acceptance, but enable to derive models of scientific communities and facilitate a better understanding of the nature of scientific work and collaboration.