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Domain-Specific Modeling: Towards a Food and Drink Gazetteer

  • Andrey Tagarev
  • Laura Toloşi
  • Vladimir Alexiev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Our goal is to build a Food and Drink (FD) gazetteer that can serve for classification of general, FD-related concepts, efficient faceted search or automated semantic enrichment. Fully supervised design of a domain-specific models ex novo is not scalable. Integration of several ready knowledge bases is tedious and does not ensure coverage. Completely data-driven approaches require a large amount of training data, which is not always available. For general domains (such as the FD domain), re-using encyclopedic knowledge bases like Wikipedia may be a good idea. We propose here a semi-supervised approach that uses a restricted Wikipedia as a base for the modeling, achieved by selecting a domain-relevant Wikipedia category as root for the model and all its subcategories, combined with expert and data-driven pruning of irrelevant categories.

Keywords

Categorization Wikipedia Wikipedia categories Gazetteer Europeana Cultural heritage Concept extraction 

Notes

Acknowledgements

The research presented in this paper was carried out as part of the Europeana Food and Drink project, co-funded by the European Commission within the ICT Policy Support Programme (CIP-ICT-PSP-2013-7) under Grant Agreement no. 621023.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Andrey Tagarev
    • 1
  • Laura Toloşi
    • 1
  • Vladimir Alexiev
    • 1
  1. 1.Ontotext ADSofiaBulgaria

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