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Domain-Specific Modeling: A Food and Drink Gazetteer

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Transactions on Computational Collective Intelligence XXVI

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 10190))

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 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.

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Notes

  1. 1.

    http://foodanddrinkeurope.eu/.

  2. 2.

    http://www.eurofir.org/.

  3. 3.

    http://www.codexalimentarius.org/standards/gsfa/.

  4. 4.

    ftp://ftp.fao.org/codex/meetings/ccpr/ccpr38/pr38CxCl.pdf.

  5. 5.

    http://www.gs1.org/gdsn/gdsn-trade-item-extension-food-and-beverage/2-8.

  6. 6.

    http://www.bbc.co.uk/ontologies/fo.

  7. 7.

    http://www.theeuropeanlibrary.org/tel4/.

  8. 8.

    http://www.rluk.ac.uk/.

  9. 9.

    http://www.sng.sk/en/uvod.

  10. 10.

    http://www.horniman.ac.uk/.

  11. 11.

    http://sourceforge.net/projects/wikipedia-miner.

  12. 12.

    http://ontotext.com/products/ontotext-graphdb/.

  13. 13.

    Customized version of http://tag.ontotext.com/.

References

  1. Agirre, E., Barrena, A., De Lacalle, O.L., Soroa, A., Fern, S., Stevenson, M.: Matching Cultural Heritage items to Wikipedia (2012)

    Google Scholar 

  2. Alexiev, V.: Europeana Food and Drink Classification Scheme, Europeana Food and Drink project, Deliverable D2.2 (2015). http://vladimiralexiev.github.io/pubs/Europeana-Food-and-Drink-Classification-Scheme-(D2.2).pdf

  3. Cheng, C.P., Lau, G.T., Pan, J., Law, K.H., Jones, A.: Domain-specific ontology mapping by corpus-based semantic similarity. In: 2008 NSF CMMI Engineering Research and Innovation Conference (2008)

    Google Scholar 

  4. Fridman Noy, N., Musen, M.A.: An algorithm for merging and aligning ontologies: automation and tool support. In: Workshop on Ontology Management at the 16th National Conference on Artificial Intelligence (AAAI 1999) (1999)

    Google Scholar 

  5. Medelyan, O., Manion, S., Broekstra, J., Divoli, A., Huang, A.-L., Witten, I.H.: Constructing a focused taxonomy from a document collection. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 367–381. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38288-8_25

    Chapter  Google Scholar 

  6. Medelyan, O., Milne, D., Legg, C., Witten, I.H.: Mining meaning from wikipedia. Int. J. Hum.-Comput. Stud. 67(9), 716–754 (2009)

    Google Scholar 

  7. Miles, A., Bechhofer, S.: SKOS simple knowledge organization system reference. W3C Recommendation, 18 August 2009

    Google Scholar 

  8. Mousavi, H., Kerr, D., Iseli, M., Zaniolo, C.: Harvesting domain specific ontologies from text. ICSC 2014, pp. 211–218 (2014)

    Google Scholar 

  9. Mousavi, H., Kerr, D., Iseli, M., Zaniolo, C.: OntoHarvester: an unsupervised ontology generator from free text, CSD Technical report #130003, University of California Los Angeles (2013)

    Google Scholar 

  10. Parekh, V., Gwo, J.: Mining domain specific texts and glossaries to evaluate and enrich domain ontologies. In: Proceedings of the International Conference of Information and Knowledge Engineering (2004)

    Google Scholar 

  11. Pinto, H.S., Martins, J.P.: A methodology for ontology integration. In: Proceedings of the 1st International Conference on Knowledge Capture, K-CAP 2001 (2001)

    Google Scholar 

  12. Ribeiro, R., Batista, F., Pardal, J.P., Mamede, N.J., Pinto, H.S.: Cooking an ontology. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS, vol. 4183, pp. 213–221. Springer, Heidelberg (2006). doi:10.1007/11861461_23

    Chapter  Google Scholar 

  13. Gubichev, A., Neumann, T.: Fast approximation of Steiner trees in large graphs. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1497–1501 (2012)

    Google Scholar 

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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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Authors and Affiliations

  1. Ontotext AD, 47A Tsarigradsko Shosse, 1124, Sofia, Bulgaria

    Andrey Tagarev, Laura Toloşi & Vladimir Alexiev

Authors
  1. Andrey Tagarev
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  2. Laura Toloşi
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  3. Vladimir Alexiev
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Corresponding author

Correspondence to Andrey Tagarev .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology , Hawthorn, South Australia, Australia

    Ryszard Kowalczyk

  3. University of Lisbon , Lisbon, Portugal

    Alexandre Miguel Pinto

  4. Huawei German Research Center, Munich, Germany

    Jorge Cardoso

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Tagarev, A., Toloşi, L., Alexiev, V. (2017). Domain-Specific Modeling: A Food and Drink Gazetteer. In: Nguyen, N., Kowalczyk, R., Pinto, A., Cardoso, J. (eds) Transactions on Computational Collective Intelligence XXVI. Lecture Notes in Computer Science(), vol 10190. Springer, Cham. https://doi.org/10.1007/978-3-319-59268-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-59268-8_9

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