BIMDANUBE: Network of Excellence on Biomedical
Information Management in the Danube Region


We establish a network of excellence on biomedical information management among some of the Danube States, and we strive to extend the initiative to multiple European countries.

Within current AI research, in BIMDANUBE, we aim to center the human within a machine learning setting, which enables interactivity between the human and the machine. The Human-In-The-Loop paradigm allows a user to take actions in order to influence a machine learning model, which improves over time through usage.

Targeting the health domain, we work closely together with experts and medical doctors and strive to a better understanding of the vast amount of data available.

During the course of BIMDANUBE, we are going to organize two workshops on current and future information management in the biomedical domain, we conduct a feasibility study on a novel bottom-up approach to information management. Our continuously growing network will prepare an EU grant proposal on novel methodologies in biomedical information management for practicioners and teaching purposes.


Recently, knowledge management as a field faced several challenges. On one hand, sophisticated technologies and standards were developed to support knowledge-based modeling, such as domain ontologies including MeSH, Disease Ontology, and Gene Ontology and the Sematic Web description languages and infrastructures including RDF, OWL, SPARQL and others. On the other hand, however the current approaches face three major issues: (1) knowledge bottleneck: required resources for knowledge management such as domain ontologies are not available for many domains and languages; (2) the overall approach of knowledge management did not get widely spread due to the fact that it imposes a large burden on the user, such as annotation or expertise with complex tools like Protégé; (3) modeling entire domains as large as the medical domain with (English-oriented) knowledge resources does not meet requirements of users, who are mostly specializing in a certain sub-field and also need to operate in their local language.

We are reloading this traditional heavyweight top-down knowledge management approach and replace it with a much simpler and practical problem-oriented bottom-up approach. We choose the biomedical domain as a playground for our experiments as this domain concerns humankind as a whole, thus being of high priority with regard to the EU Horizon 2020 strategic plan. Further, we deem the Danube region with its high variability in languages and heterogeneity as an ideal test bed for our approach.

Medical researchers have to process an enormous amount of the literature – PubMed adds about half a million paper to its index each year. Literature search and reasoning is demanding, because of the need to revealing and maintaining many complex relationships between numerous sets of entities. In order to alleviate the efforts of biomedical research related to literature we propose a novel conception to information management based on bottom-up construction of a problem-oriented ontology, called entity graph (EG). Entity graphs provide a new tool for medical researchers that (1) help to document relations between biomedical entities in a compact intuitive and interpretable form; (2) generate new relations in a semi-automatic way based on corpus analysis; (3) communicate new biomedical knowledge in a form of an easily interpretable interactive graph and (4) share knowledge and annotations amongst researchers.


Our project originates from collaboration between researchers from Danube states, namely Germany, Austria, Croatia, and Bulgaria. Our collaboration is an excellent example for establishment of a network of excellence in the biomedical domain across the Danube region. Our ultimate goal is to create a network of mutually beneficial scientific collaborations in the region around the biomedical information management topic.

Chris Biemann



Andreas Holzinger



Ljiljana Majnarić



Svetla Boytcheva



Steffen Remus



1st Workshop on Biomedical Information Management: Challenges and Open Problems

The workshop will gather interested professionals from the Danube region and across Europe working in the information or medical domain, such as medical researchers, medical doctors and entrepreneurs building their business around biomedical ICT. The goal of the workshop is to obtain answers to the following questions:

  • What information and knowledge management solutions are actively used in the community?
  • What are limitations of the current solutions?
  • What important problems in biomedical information management are not addressed and automatized at all by any solution?

Besides the invited speakers, the workshop will feature regular participants from the Danube states who are interested in the subject of biomedical information management and keen to establish a new project in this area.

Invited Speakers:

Phillipp Cimiano


Daniel Keim


Ulf Leser


Udo Hahn


Hercules Dalianis


Sophia Ananiadou


WHEN 19.   &   20.   Feb.   2018
WHERE University of Hamburg, Hamburg, Germany

More information is availbable on the official workshop hompage.

2nd Workshop on Biomedical Information Management: Data-Driven Innovations

The event will be more technical in nature than the first workshop. The goal of the second workshop will be to answer on the following questions:

  • What are the prominent technological solutions to the problems identified during the first workshop?
  • What recent insights from Machine Learning, Data Mining and Natural Language Processing can be used to innovate in the biomedical information management?

Besides the invited speakers, the workshop will feature regular participants from the Danube states who are interested in the subject of biomedical information management and keen to establish a new project in this area.

Participation is free but registration is required.

Invited Speakers:







WHEN August 30.   &   31. 2018

Universität Hamburg
Heilwigstraße 116
20249 Hamburg

More information is availbable on the official workshop hompage.


The team at the University of Hamburg with help of collaborators will drive this study. The goal of the experiment is to try to reload the traditional top-down knowledge management approaches, involving large domain ontologies. The entity graph can be considered as a personalized problem-oriented ontology, created during investigation of a narrow problem. The main advantage of the entity graph is that it is much simpler, easy-to-use and goal-oriented with respect to the classical ontologies and other heavyweight knowledge management techniques. During the second workshop, we are going to present and discuss bottom-up approach to biomedical information management devised in this study.

The team at the University of Hamburg will build upon multiple prior developments to perform this study efficiently. This team is going to implement the software prototype required for the experiment and for the overall design of the experiment. The feasibility study will be conducted on biomedical researchers from the partner institutions in Austria, Croatia and Bulgaria.

Our prototype presents a novel way to deal with biomedical knowledge based on entity graphs, thus introducing and evaluating a novel human-computer interaction paradigm for personal and shared information management. This new paradigm, in combination with the free availability of our prototype, might have a disruptive effect on the content management and knowledge/information management industries, which still largely operate in ontology/taxonomy/vocabulary-driven ways. The new concept of bottom-up information management can create an ecosystem of providers that offer software solutions for the integration of this concept with existing systems. Further, there are considerable business opportunities in maintaining distributed repositories, as well as interlinking them.


  • Steffen Remus, Manuel Kaufmann, Kathrin Ballweg, Tatiana von Landesberger, and Chris Biemann. 2017. Storyfinder: Personalized Knowledge Base Construction and Management by Browsing the Web. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management. Singapore, Singapore. To appear.
  • Seid M Yimam, Steffen Remus, Alexander Panchenko, Andreas Holzinger, Chris Biemann. 2017. Entity-Centric Information Access with the Human-in-the-Loop for the Biomedical Domains. In Biomedical NLP Workshop associated with RANLP 2017. Varna, Bulgaria
  • Seid M Yimam, Richard Eckart de Castilho, Iryna Gurevych, and Chris Biemann. 2014. Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno. In Proceedings of ACL-2014. Baltimore, MD, USA
  • Seid M Yimam, Heiner Ulrich, Tatiana von Landesberger, Marcel Rosenbach, Michaela Regneri, Alexander Panchenko, Franziska Lehmann, Uli Fahrer, Chris Biemann, and Kathrin Ballweg. 2016. new/s/leak – Information Extraction and Visualization for an Investigative Data Journalists. In ACL 2016 Demo Session. Berlin, Germany
  • Artjom Kochtchi, Tatiana von Landesberger, and Chris Biemann. 2014. Networks of names: Visual exploration and semi-automatic tagging of social networks from newspaper articles. In Eurographics Conference on Visualization (EuroVis) and Computer Graphics Forum.
  • Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crisan, Camelia-M. Pintea, and Vasile Palade. 2017. A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop. arXiv:1708.01104.
  • Andreas Holzinger. 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131.
  • Peter Kieseberg, Edgar Weippl, and Andreas Holzinger. 2016. Trust for the Doctor-in-the-Loop. European Research Consortium for Informatics and Mathematics (ERCIM) News: Tackling Big Data in the Life Sciences, 104, (1), 32-33.
  • Michael Hund, Dominic Böhm, Werner Sturm, Michael Sedlmair, Tobias Schreck, Torsten Ullrich, Daniel A. Keim, Ljiljana Majnaric, and Andreas Holzinger. 2016. Visual analytics for concept exploration in subspaces of patient groups: Making sense of complex datasets with the Doctor-in-the-loop. Brain Informatics, 3, (4), 233-247.


Steffen Remus
Hamburg, DE
+49 40 42883 2369
remus  informatik  uni–hamburg  de