My research interest is Web Science, “the emergent science of the
people, organizations, applications, and of policies that shape and
are shaped by the Web, the largest informational artifact constructed
by humans in history” (from the
call for papers of the ACM Web
Science conference). Thereby, my research is situated in computer
science, with multi-disciplinary connections to psychology, sociology,
economics, and the digital humanities.
More specifically, together with colleagues from the Leibniz Research
Alliance Open Science, I am
investigating, how the (social) web is changing the research landscape
and how it can improve communication, collaboration, participation,
and open discourse.
I am also leading the development of the collaborative tagging system
BibSonomy, which is both a valuable tool for researchers
to organize their literature as well as a test-bed for our methods and
results. In that context, I am interested in the development and
integration of recommendation methods for tags and scientific
publications for social bookmarking systems. Further topics of
interest include citation and link analysis, entity matching and
resolution, and social network analysis.
I extensively leverage big data technologies like Hadoop, HBase,
Drill, or Elasticsearch for my research, e.g., to analyze crawled web
pages of universities in the context of Open Science. Therefore, I
have designed a dedicated cluster system for L3S Research
Center, consisting of 40 nodes with an overall
disk space of 2 Petabyte and 400 CPU cores. Since 2013 the first
stages are installed and I am managing the operation and further
extension of the cluster.
Our social bookmark and publication sharing system
BibSonomy is online since 2006 with me being the main
developer from 2005 to 2012. Since 2009 I am leading the development
and operation of the system together with Andreas
Hotho. If you
are interested in a cooperation, just let me know.
Together with the University Library Kassel we have extended the
BibSonomy platform in the DFG-funded
PUMA project for academic publication
management. If you are interested in using PUMA, please
contact us ().
We are developing novel methods to identify and extract Vossian
antonomasia from large newspaper
corpora. The approaches based on deep learning enable us to study this
linguistic device on a large scale. Code, data, statistics, and many
examples are available on our project page and GitHub repository.
FolkRank is an algorithm for search and
in collaborative tagging systems. It has been integrated into the
community support architecture of the social semantic desktop
developed by the NEPOMUK
project. The source code is
available from the project’s SVN