UIMA2LOD: Integrating UIMA Text Annotations into the Linked Open Data Cloud

  • Claudia BretschneiderEmail author
  • Heiner Oberkampf
  • Sonja Zillner
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 518)


The LOD cloud is becoming the de-facto standard for sharing and connecting pieces of data, information and knowledge on the Web. As of today, means for the seamless integration of structured data into the LOD cloud are available. However, algorithms for integrating information enclosed in unstructured text sources are missing. In order to foster the (re)use of the high percentage of unstructured text, automatic means for the integration of their content are needed. We address this issue by proposing an approach for conceptual representation of textual annotations which distinguishes linguistic from semantic annotations and their integration. Additionally, we implement a generic UIMA pipeline that automatically creates a LOD graph from texts that (1) implements the proposed conceptual representation, (2) extracts semantically classified entities, (3) links to existing LOD datasets and (4) generates RDF graphs from the extracted information. We show the application and benefits of the approach in a case study on a medical corpus.


Open Annotation Semantic Annotation Name Entity Recognition Entity Recognition Text Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Claudia Bretschneider
    • 1
    • 2
    Email author
  • Heiner Oberkampf
    • 2
  • Sonja Zillner
    • 2
    • 3
  1. 1.Center for Information and Language ProcessingUniversity MunichMunichGermany
  2. 2.Siemens AG, Corporate TechnologyMunichGermany
  3. 3.School of International Business and EntrepreneurshipSteinbeis UniversityBerlinGermany

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