![](bg9.png)
reference alignments, corresponding to the complete
alignment space between 7 ontologies from the
conference data set. Table 6 shows the values of
precision, recall and f-measure obtained by the 7
systems that we compared, and the confidence
threshold set by each of them to provide the highest
average of f-measure. In the case of CODI they not
provided a confidence threshold because their results
were the same regardless of the threshold. We can
observe that with a confidence threshold of 0.8 our
system scored precision, recall and f-measure much
higher than others. This means that we are
considering as valid alignment only those mappings
whose similarity value is greater than 80%, which
shows that the system has shown better results with
a greater level of rigor in the selection of alignments.
Table 6: Conference test results for the alignment methods
in terms of confidence threshold, precision, recall and f-
measure.
System Threshold P R F
AgrMaker 0.66 0.53 0.62 0.58
ASMOV 0.22 0.57 0.63 0.60
CODI - 0.86 0.48 0.62
Ef2Match 0.84 0.61 0.58 0.60
GeRMeSMB 0.87 0.37 0.51 0.43
SOBOM 0.35 0.56 0.56 0.56
FuzzyAlign 0.80 0.93 0.74 0.83
7 CONCLUSIONS
AND FUTURE WORK
This article describes our work aimed at providing a
method to assist experts in the ontology alignment
process using fuzzy logic techniques. We propose
FuzzyAlign, a Multi-Layer Fuzzy System which
computes the similarities between entities from
different ontologies, taking into account semantic
and lexical elements and also the relational and the
internal structures of the ontologies. The system has
been tested in three of the basic tests proposed for
OAEI to evaluate the performance of ontology
alignment methods, showing better results than
others systems in general purpose ontologies and
ontologies from real life with correct lexical
constructions.
Through our experiments yield satisfactory
results, there are some limitations inherent to our
approach. Due to the importance of linguistic in the
process of matching and the use of WordNet, the
system not provides optimal results in very specific
domain ontologies. In addition the execution time of
the system increases when processing too large
ontologies due to the high amount of information.
Finally, as future work we intend to improve the
scalability of the application. We plan also to use
more linguistic tools, such as other lexical domain-
specific directories, like the Unified Medical
Language System (UMLS) metathesaurus for
medical ontologies, and thus ensure better results in
this types of ontologies. We also are interested in
extending the technique to propose an integration
model that allows matching taking into account the
use of other relations in real domains instead of just
equivalence.
ACKNOWLEDGEMENTS
This work is part of the RESULTA Project,
supported by the Spanish Ministry of Industry,
Tourism and Trade, TSI-020301-2009-31.
REFERENCES
Cordón, O., Herrera, F., Hoffman, F., Magdalena, L.
(2001): Genetic Fuzzy Systems. Evolutionary Tuning
and Learning of Fuzzy Knowledge Bases. World
Scientific, Singapore 2001.
Cruz, Isabel F., Palandri, Antonelli Flavio, and Stroe,
Cosmin. (September 2009): AgreementMaker
Efficient Matching for Large Real-World Schemas and
Ontologies. In International Conference on Very
Large Databases, Lyon, France, pages 1586-1589,
Doan A., Madhavan, J., Domingos, P., Halevy, A.,
(2004).: Ontology Matching: A Machine Learning
Approach. Handbook on Ontologies in Information
Systems. In: S. Staab and R. Studer (eds.), Invited
paper. Pp. 397-- 416. Springer-Velag
Euzenat, J., Ferrara, A., Hollink, L., Isaac, A., Joslyn, C.,
Malais´e, V., Meilicke, C., Nikolov, A., Pane, J.,
Sabou, M., Scharffe, F., Shvaiko, P., Spiliopoulos, V.,
Stuckenschmidt, H., ˇSv´ab-Zamazal, O., Sv´atek, V.,
dos Santos, C.T., Vouros, G., Wang, S. (2009) :
Results of the Ontology Alignment Evaluation
Initiative 2009. In: Proceedings of the 4th
International Workshop on Ontology Matching (OM-
2009). vol. 551. CEUR Workshop Proceedings,
http://ceur-ws.org
Euzenat, J, Shvaiko, P., Giunchiglia, F., Stuckenschmidt,
H., Mao, M., Cruz, I. (2010): Results of the Ontology
Alignment Evaluation Initiative 2010. In: Proceedings
of the 5th International Workshop on Ontology
Matching (OM-2010).
Fellbaum, C. (1998). WordNet: An Electronic Lexical
Database. MIT Press. 3.0, Cambridge, MA.
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