![](bg8.png)
7 CONCLUSIONS
This paper presents an ontology-learning process
which uses personalized dietary web services in or-
der to develop an ontology that can be used for the
harmonization of personalized dietary web services
and will enable researchers to share information in
this domain. Having this kind of ontology will en-
able researchers to use aggregated data and informa-
tion from different sources to provide new knowledge,
new protocols and help people live healthier lives.
ACKNOWLEDGEMENTS
This work was supported by the project QuaLiFY,
which received funding from the European Union’s
Seventh Framework Programme for research, techno-
logical development and demonstration under grant
agreement no 613783. This work was supported
by the project ISO-FOOD, which received fund-
ing from the European Union’s Seventh Frame-
work Programme for research, technological devel-
opment and demonstration under grant agreement no
621329 (2014-2019). This work was conducted us-
ing the Prot
´
eg
´
e resource, which is supported by grant
GM10331601 from the National Institute of General
Medical Sciences of the United States National In-
stitutes of Health. We would like to thank the Co-
founder of SafeCape Software Solutions Ltd, Agge-
los Androulidakis, for helping us with the SafeCape
web service, the Hyve and the TNO for helping us
with the Food4Me web service. The Hyve is a SME
working on development and professional support for
open source software used in life science research
that created the IT-part and the TNO is an indepen-
dent research organisation that adapted the input of
Food4Me to fit both the web-service.
REFERENCES
Biemann, C. (2005). Ontology learning from text: A survey
of methods. In LDV forum, volume 20, pages 75–93.
Boulos, M. N. K., Yassine, A., Shirmohammadi, S., Nama-
hoot, C. S., and Br
¨
uckner, M. (2015). Towards an
“internet of food”: Food ontologies for the internet of
things. Future Internet, 7(4):372–392.
Caracciolo, C., Stellato, A., Rajbahndari, S., Morshed,
A., Johannsen, G., Jaques, Y., and Keizer, J. (2012).
Thesaurus maintenance, alignment and publication as
linked data: the agrovoc use case. International Jour-
nal of Metadata, Semantics and Ontologies, 7(1):65–
75.
C¸ elik, D. (2015). Foodwiki: Ontology-driven mobile safe
food consumption system. The Scientific World Jour-
nal, 2015.
Eftimov, T. and Seljak, B. K. (2015). Pos tagging-
probability weighted method for matching the inter-
net recipe ingredients with food composition data. In
Knowledge Discovery, Knowledge Engineering and
Knowledge Management (IC3K), 2015 7th Interna-
tional Joint Conference on, volume 1, pages 330–336.
IEEE.
Greenfield, H. and Southgate, D. A. (2003). Food composi-
tion data: production, management, and use. Food &
Agriculture Org.
Hazman, M., El-Beltagy, S. R., and Rafea, A. (2011). A sur-
vey of ontology learning approaches. database, 7:6.
Hellwig, J. P., Otten, J. J., Meyers, L. D., et al. (2006). Di-
etary Reference Intakes:: The Essential Guide to Nu-
trient Requirements. National Academies Press.
Kolchin, M. and Zamula, D. (2013). Food product ontol-
ogy: Initial implementation of a vocabulary for de-
scribing food products. In Proceeding of the 14th
Conference of Open Innovations Association FRUCT,
Helsinki, Finland, pages 11–15.
Noy, N. F., McGuinness, D. L., et al. (2001). Ontology
development 101: A guide to creating your first ontol-
ogy.
Real, R. and Vargas, J. M. (1996). The probabilistic basis
of jaccard’s index of similarity. Systematic biology,
pages 380–385.
Schmid, H. (1994). Probabilistic part-of-speech tagging us-
ing decision trees. In Proceedings of the international
conference on new methods in language processing,
volume 12, pages 44–49. Citeseer.
Snae, C. and Br
¨
uckner, M. (2008). Foods: a food-oriented
ontology-driven system. In Digital Ecosystems and
Technologies, 2008. DEST 2008. 2nd IEEE Interna-
tional Conference on, pages 168–176. IEEE.
Tian, Y. and Lo, D. (2015). A comparative study on the
effectiveness of part-of-speech tagging techniques on
bug reports. In Software Analysis, Evolution and
Reengineering (SANER), 2015 IEEE 22nd Interna-
tional Conference on, pages 570–574. IEEE.
Voutilainen, A. (2003). Part-of-speech tagging. The Oxford
handbook of computational linguistics, pages 219–
232.
KEOD 2018 - 10th International Conference on Knowledge Engineering and Ontology Development
286