Volume 4 Supplement 1

7th German Conference on Chemoinformatics: 25 CIC-Workshop

Open Access

Large scale chemical patent mining with UIMA and UNICORE

  • Alexander Klenner1Email author,
  • Sandra Bergmann2,
  • Marc Zimmermann1 and
  • Mathilde Romberg2
Journal of Cheminformatics20124(Suppl 1):P19

DOI: 10.1186/1758-2946-4-S1-P19

Published: 1 May 2012

Finding information about annotated chemical reactions for drugs and small compounds is a crucial step for pharmaceutical industries. This data often is presented in form of unstructured documents (especially patents) and manual extraction of this information is a time- and cost inefficient effort.

In our project UIMA-HPC [1], we describe the combined usage of Unstructured Information Managment Architecture (UIMA) and Uniform Interface to Computing Recources (UNICORE) for large-scale chemical patent mining. Our approach will incorporate existing software such as chemoCR for image processing (image-to-structure) and OCR for text reconstruction. All components are wrapped inside the UIMA framework pipeline. Using the UIMA framework ensures compatibility between different components of the pipeline and makes it possible to connect arbitrary annotation modules into this system. Scale-out for large document collections is achieved by the UNICORE framework on High Performance Clusters, which enables parallelization of all UIMA nodes. The aim is a fully annotated pdf collection where all biomedical entities (compound names, reaction schemes, etc.) are connected by references and thus can be easily browsed and searched by the user. Planned schematic workflow is shown in Figure 1.
https://static-content.springer.com/image/art%3A10.1186%2F1758-2946-4-S1-P19/MediaObjects/13321_2012_Article_290_Fig1_HTML.jpg
Figure 1

Planned workflow of our UIMA framework. 'Recognition' and 'annotation' are CPU intensive parts that are parallelized on demand using the UNICORE framework. 'Merging' checks for cross-annotations (entity in text and image). Finally, an annotated PDF is presented as output.

Funding

BMBF grant 01IH1101.

Authors’ Affiliations

(1)
Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI)
(2)
Forschungszentrum Juelich GmbH

References

Copyright

© Klenner et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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