![](bg4.png)
fuzzy logic, a wine produced in year 2008 or 2010
would still be considered and not be eliminated from
the search already in an early stage.
4 CONCLUSIONS
In this article, we present a short literature review
about fuzzy ontologies. In recent years, there have
been several successful applications of fuzzy logic
and ontologies. The fact that fuzzy logic can deal
with uncertain and imprecise knowledge in a more ef-
ficient way, compared to traditional methods, is a key
factor for encouraging future development. Research
results prove that applications and systems based on
fuzzy logic and ontologies have the possibility to cap-
ture and model tacit knowledge. This could limit the
loss of expert knowledge, for instance, as employees
disappear from organizations.
A web application, based on the techniques and
methods developed by Bobillo and Straccia (2011)
and Bobillo and Straccia (2008) in combination with
Java, Gurobi, HTML and Protégé, is also presented.
The application can be accessed and used through a
web browser. As the need for accessing relevant in-
formation wherever and whenever one needs it is in-
creasing, the mobility issues will certainly be impor-
tant factors for future applications. This web applica-
tion shows that fuzzy ontologies and fuzzy reasoners
can be accessed and used through web browser; in
other words, the application is made platform inde-
pendent.
Future directions for the fuzzy ontology fields
seem to be centred around the combination between
fuzzy logic and the Web Ontology Language (OWL).
The latest research and findings on type-2 fuzzy logic
together with ontologies should spark future research
about type-2 fuzzy logic based ontologies, as this
combination improves the possibilities to model un-
certainty (Lee et al., 2010). Exploring how this recent
development could facilitate future knowledge mobi-
lization applications and systems is therefore the cen-
tral theme for future research.
ACKNOWLEDGEMENTS
A special thanks to Prof. Christer Carlsson (IAMSR)
and Dr. József Mezei (IAMSR) for valuable advices
regarding the article.
REFERENCES
Bobillo, F., Delgado, M., and Gómez-Romero, J. (2012).
Delorean: A reasoner for fuzzy owl 2. Expert Systems
with Applications, 39(1):258–272.
Bobillo, F. and Straccia, U. (2008). fuzzydl: An expressive
fuzzy description logic reasoner. FUZZ-IEEE, pages
923–930.
Bobillo, F. and Straccia, U. (2011). Fuzzy ontology rep-
resentation using owl 2. International Journal of Ap-
proximate Reasoning, 52(7):1073–1094.
Calegari, S. and Ciucci, D. (2007). Fuzzy ontology, fuzzy
description logics and fuzzy-owl. Applications of
Fuzzy Sets Theory, pages 118–126.
Carlsson, C., Brunelli, M., and Mezei, J. (2012a). Decision
making with a fuzzy ontology. Soft Computing-A Fu-
sion of Foundations, Methodologies and Applications,
pages 1–10.
Carlsson, C., Mezei, J., and Brunelli, M. (2012b). Fuzzy
ontology used for knowledge mobilization. Interna-
tional Journal of Intelligent Systems, 28(1):52–71.
d‘Aquin, M. and Noy, N. F. (2012). Where to publish and
find ontologies? a survey of ontology libraries. Web
Semantics: Science, Services and Agents on the World
Wide Web, 11(0):96 – 111.
Hebeler, J., Fisher, M., Blace, R., and Perez-Lopez, A.
(2011). Semantic web programming. Wiley.
Kordon, A. (2009). Applying computational intelligence:
how to create value. Springer.
Lee, C., Wang, M., Acampora, G., Hsu, C., and Hagras, H.
(2010). Diet assessment based on type-2 fuzzy ontol-
ogy and fuzzy markup language. International Jour-
nal of Intelligent Systems, 25(12):1187–1216.
Machacha, L. and Bhattacharya, P. (2000). A fuzzy-logic-
based approach to project selection. Engineering
Management, IEEE Transactions on, 47(1):65–73.
Peña, L. (1984). Identity, fuzziness and noncontradiction.
Nous, 18(2):227–259.
Sanchez, E. (2006). Fuzzy Logic and the Semantic Web,
volume 1. Elsevier Science.
Stoilos, G., Stamou, G., Tzouvaras, V., Pan, J., and Hor-
rocks, I. (2005). Fuzzy owl: Uncertainty and the se-
mantic web. In Proc. of the International Workshop
on OWL: Experiences and Directions, volume 280.
Straccia, U. (2010). Softfacts: a top-k retrieval engine for
ontology mediated access to relational databases. In
Systems Man and Cybernetics (SMC), 2010 IEEE In-
ternational Conference on, pages 4115–4122. IEEE.
Yager, R. R. (1988). On ordered weighted averaging ag-
gregation operators in multicriteria decision making.
IEEE Transactions on Systems, Man, and Cybernet-
ics, 18(1):183–190.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control,
8(3):338–353.
OntologyofImprecisionandFuzzyOntologyApplications
287