@inproceedings{1144,
  abstract     = {{Adopting the concept of “Local Grammars” (M. Gross), which were successfully applied in practice by (Geierhos, 2010) to biographical information extraction in English our project aims to detect, encode, and finally visualize relations between persons. Our corpus consists of the digitised biographical lexicon “Neue Deutsche Biographie (NDB)”, roughly 21.000 biographies in 25 volumes in print since 1953. We developed local grammars and suitable dictionaries to describe interpersonal relations and applied them to the corpus with Unitex 3.1. The local grammars were designed to integrate existing TEI-XML structures in the corpus. Using the ability of local grammars in Unitex to act as transducers we were able to produce XML-Tags and encode semantic information. Based on grammars for personal names and places we described interpersonal relations like to study, predecessors and successors as well as friends and circles. Afterwards we
identified persons (as given in the authority file or index). Finally we displayed relations on our website in an interactive and dynamic way. Utilizing the Javascript library D3.js we represented named relations between identified individuals as ego centred network graphs.}},
  author       = {{Stotz, Sophia and Stuß, Valentina and Reinert , Matthias and Schrott, Maximilian}},
  booktitle    = {{Proceedings of the First Conference on Biographical Data in a Digital World 2015}},
  editor       = {{ter Braake, Serge and Fokkens, Antske and Sluijter, Ronald and Declerck, Thierry and Wandl-Vogt, Eveline}},
  issn         = {{16130073}},
  keywords     = {{Local Grammar, Relation Extraction, Visualisation}},
  location     = {{Amsterdam, Netherlands}},
  pages        = {{74--80}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Interpersonal relations in biographical dictionaries. A case study}}},
  volume       = {{1399}},
  year         = {{2015}},
}

@inproceedings{1134,
  abstract     = {{This paper focuses on the first step in combining prescriptive analytics with scenario techniques in order to provide strategicdevelopment after the useof InSciTe, a data prescriptive analytics application. InSciTe supports the improvement of researchers‘ individual performance by recommending new research directions. Standardized influential factors are presented as a foundation for automated scenario modelling such as the prototypical report generation function of InSciTe. Additionally, a use-case is shown which validatesthe potential of the standardized influential factors for raw scenario development.}},
  author       = {{Weber, Jens and Minhee, Cho and Lee, Mikyoung and Song, Sa-kwang and Geierhos, Michaela and Jung, Hanmin}},
  booktitle    = {{Proceedings of the First International Workshop on Patent Mining and Its Applications (IPaMin 2014) co-located with Konvens 2014}},
  editor       = {{Jung, Hanmin and Mandl, Thomas and Womsen-Hacker, Christa and Xu, Shuo}},
  issn         = {{16130073}},
  keywords     = {{Standardized Influential Factors, Prescriptive Analytics, Role Model Group, Scenario Technique}},
  location     = {{Hildesheim, Germany}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{System Thinking: Crafting Scenarios for Prescriptive Analytics}}},
  volume       = {{1292}},
  year         = {{2014}},
}

@inproceedings{1137,
  abstract     = {{In this paper, we present a system which makes scientific data available following the linked open data principle using standards like RDF and URI as well as the popular D2R server (D2R) and the customizable D2RQ mapping language. Our scientific data sets include acronym data and expansions, as well as researcher data such as author name, affiliation, coauthors, and abstracts. The system can easily be extended to other records. Regarding this, a domain adaptation to patent mining seems possible. For this reason, obvious similarities and differences are presented here. The data set is collected from several different providers like publishing houses and digital libraries, which follow different standards in data format and structure. Most of them are not supporting semantic web technologies, but the legacy HTML standard. The integration of these large amounts of scientific data into the Semantic Web is challenging and it needs flexible data structures to access this information and interlink them. Based on these data sets, we will be able to derive a general technology trend as well as the individual research domain for each researcher. The goal of our Linked Open Data System for scientific data is to provide access to this data set for other researchers using the Web of Linked Data. Furthermore we implemented an application for visualization, which allows usto explorethe relations between single data sets.}},
  author       = {{Bäumer, Frederik Simon and Gim, Jangwon and Jeong, Do-Heon and Geierhos, Michaela and Jung, Hanmin}},
  booktitle    = {{Proceedings of the First International Workshop on Patent Mining and Its Applications (IPaMin 2014) co-located with Konvens 2014}},
  editor       = {{Jung, Hanmin and Mandl, Thomas and Womsen-Hacker, Christa and Xu, Shuo}},
  issn         = {{16130073}},
  keywords     = {{Linked Open Data, Researcher Data, Acronym Data, D2R}},
  location     = {{Hildesheim, Germany}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Linked Open Data System for Scientific Data Sets}}},
  volume       = {{1292}},
  year         = {{2014}},
}