IPKB: a digital library for invertebrate paleontology

  • Authors:

  • Yuanliang Meng;Junyan Li;Patrick Denton;Yuxin Chen;Bo Luo;Paul Selden;Xue-wen Chen

  • Affiliations:

  • University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;Swiss Federal Institute of Technology, Zurich, Switzerland;Universit of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA

  • Venue:
  • Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
  • Year:
  • 2012

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Abstract

In this paper, we present the Invertebrate Paleontology Knowledgebase (IPKB), an effort to digitize and share the Treatise on Invertebrate Paleontology. The Treatise is the most authoritative compilation of invertebrate fossil records. Unfortunately, the PDF version is simply a clone of paper publications and the content is in no way organized to facilitate search and knowledge discovery. We extracted texts and images from the Treatise, stored them in a database, and built a system for efficient browsing and searching. For image processing in particular, we segmented fossil photos from figures, recognized the embedded labels, and linked the images to the corresponding data entries. The detailed information of each genus, including fossil images, is delivered to users through a web access module. Some external applications (e.g. Google Earth) are acquired through web services APIs to improve user experience. Given the rich information in the Treatise, analyzing, modeling and understanding paleontological data are significant in many areas, such as: understanding evolution; understanding climate change; finding fossil fuels, etc. IPKB builds a general framework that aims to facilitate knowledge discovery activities in invertebrate paleontology, and provides a solid foundation for future explorations. In this article, we report our initial accomplishments. The specific techniques we employed in the project, such as those involved in text parsing, image-label association and meta data extraction, can be insightful and serve as examples for other researchers.