=Paper=
{{Paper
|id=Vol-551/paper-10
|storemode=property
|title=AROMA results for OAEI 2009
|pdfUrl=https://ceur-ws.org/Vol-551/oaei09_paper3.pdf
|volume=Vol-551
|dblpUrl=https://dblp.org/rec/conf/semweb/David08a
}}
==AROMA results for OAEI 2009==
https://ceur-ws.org/Vol-551/oaei09_paper3.pdf
AROMA results for OAEI 2009
Jérôme David1
Université Pierre-Mendès-France, Grenoble
Laboratoire d’Informatique de Grenoble
INRIA Rhône-Alpes, Montbonnot Saint-Martin,
France
Jerome.David-at-inrialpes.fr
Abstract. This paper presents the results obtained by AROMA for its second
participation to OAEI. AROMA is an hybrid, extensional and asymmetric on-
tology alignment method that makes use of the association paradigm and a sta-
tistical interestingness measure, the implication intensity. AROMA performs a
post-processing step that includes a terminological matcher. This year we modify
this matcher in order to improve the recall obtained on real-case ontology, i.e.
anatomy and 3xx tests.
1 Presentation of AROMA
1.1 State, purpose, general statement
AROMA is an hybrid, extensional and asymmetric matching approach designed to find
out relations of equivalence and subsumption between entities, i.e. classes and prop-
erties, issued from two textual taxonomies (web directories or OWL ontologies). Our
approach makes use of the association rule paradigm [Agrawal et al., 1993], and a sta-
tistical interestingness measure. AROMA relies on the following assumption: An entity
A will be more specific than or equivalent to an entity B if the vocabulary (i.e. terms and
also data) used to describe A, its descendants, and its instances tends to be included in
that of B.
1.2 Specific techniques used
AROMA is divided into three successive main stages: (1) The pre processing stage
represents each entity, i.e. classes and properties, by a set of terms, (2) the second stage
consists of the discovery of association rules between entities, and finally (3) the post
processing stage aims at cleaning and enhancing the resulting alignment.
The first stage constructs a set of relevant terms and/or datavalues for each class
and property. To do this, we extract the vocabulary of class and property from their
annotations and individual values with the help of single and binary term extractor
applied to stemmed text. In order to keep a morphism between the partial orders of class
and property subsumption hierarchies in one hand and the inclusion of sets of term in
the other hand, the terms associated with a class or a property are also associated with
its ancestors.
The second stage of AROMA discovers the subsumption relations by using the
association rule model and the implication intensity measure [Gras et al., 2008]. In the
context of AROMA, an association rule a → b represents a quasi-implication (i.e. an
implication allowing some counter-examples) from the vocabulary of entity a into the
vocabulary of the entity b. Such a rule could be interpreted as a subsumption relation
from the antecedent entity toward the consequent one. For example, the binary rule
car → vehicle means: ”The concept car is more specific than the concept vehicle”.
The rule extraction algorithm takes advantage of the partial order structure provided
by the subsumption relation, and a property of the implication intensity for pruning the
search space.
The last stage concerns the post processing of the association rules set. It performs
the following tasks:
– deduction of equivalence relations,
– suppression of cycles in the alignment graph,
– suppression of redundant correspondences,
– selection of the best correspondence for each entity (the alignment is an injective
function),
– the enhancement of the alignment by using a string similarity -based matcher and
previously discovered correspondences.
This year, we made some changes on the string similarity -based matcher. These
changes are primarily designed to improve the recall on anatomy track. Now AROMA
includes an equality -based matcher: two entities are considered equivalent if they share
at least one annotation. This matcher is only applied on unaligned pairs of entities.
The string similarity based matcher still makes use of Jaro-Winkler similarity but
relies on a different weighting scheme. As an ontology entity is associated to a set
of annotations, i.e. local name, labels and comments, we need a collection measure
for aggregating the similarity values between all entity pairs. Last year, we relied on
maximal weight maximal graph matching collection measure, see [David and Euzenat,
2008] for details.
In order to favour the measure values of most similar annotations pairs, we choose
to use the following collection measure:
P
Pa∈T (e) arg maxa0 ∈T (e0 ) simjw (a,a0 )2 if |T (e)| ≤ |T (e0 )|
0 0
a∈T (e) arg maxa0 ∈T (e0 ) simjw (a,a )
∆mw (e, e ) =
∆ (e0 , e) otherwise
mw
where T (e) is the set which contains the annotations and the local name of e, and simjw
is the Jaro-Winkler similarity. For all OAEI tracks, we choose a threshold value of 0.8.
For more details about AROMA, the reader should refer to [David et al., 2007;
David, 2007].
1.3 Link to the system and parameters file
The version 1.1 of AROMA has been used for OAEI2009. This version can be down-
loaded at : http://gforge.inria.fr/frs/download.php/23649/AROMA-1.1.zip.
The command line used for aligning two ontologies is:
java -jar aroma.jar onto1.owl onto2.owl [alignfile.rdf]
The resulting alignment is provided in the alignment format.
1.4 Link to the set of provided alignments (in align format)
http://www.inrialpes.fr/exmo/people/jdavid/oaei2009/results_AROMA_oaei2009.zip
2 Results
We participated to the benchmark, anatomy and conference tracks. We used the same
configuration of AROMA for all tracks. We did not experience scaling problem. Since
AROMA relies on syntactical data without using any multilingual resources, it is not
able to find alignment on the multilingual library track. Finally, we also did not par-
ticipate either to the instance matching track since AROMA is not designed for such a
task.
2.1 Benchmark
Since AROMA mainly relies on textual information, it obtains bad recall values when
the alterations affect all text annotations both in the class/property descriptions and in
their individual/property values. AROMA does not seem to be influenced by structural
alterations ( 222-247). On these tests, AROMA favours high precision values in com-
parison to recall values.
In comparison with last year, the modification made on AROMA have a limited
negative impact on 2xx tests. By contrast, the results on 3xx tests have been enhanced:
from 82% of precision and 71% of recall to respectively 85% and 78%.
2.2 Anatomy
On anatomy test, we do not use any particular knowledge about biomedical domain.
AROMA runs quite fast since it takes benefits of the subsumption relation for pruning
the search space. We further optimized the code since last year and now AROMA needs
around 1 min. to compute the alignment. This pruning feature used by AROMA par-
tially explained the low recall values obtained last year. For this edition, we enhanced
the recall by using also an string equality based matcher before using the lexical simi-
larity based matcher. Since AROMA returns not only equivalence correspondences but
also subsumption correspondences, its precision value is negatively influenced. It could
be interesting to evaluate results by using semantic precision and recall.
3 General comments
3.1 Comments on the OAEI test cases
In this section, we give some comments on the directory and oriented matching tracks
of OAEI.
Directory The two large directories, that were given in previous editions of OAEI,
are divided into very small sub directories. AROMA cannot align such very small di-
rectories because our method is based on a statistical measure and then it needs some
large amount of textual data. However, AROMA discovers correspondences when it is
applied to the complete directories. It would be interesting to reintroduce these large
taxonomies for the next editions.
Oriented matching We did not participate to this track because we think that it is not
well designed. Indeed, the proposed reference alignments are not complete.
For example in the 303 test, the reference alignment contains:
– 101#MastersThesis ≤ 103#Academic
– 103#MastersThesis ≤ 101#Academic
Obviously, no reliable matching algorithm would return these two correspondences
but rather:
– 101#MastersThesis ≡103#MastersThesis
– 101#Academic ≡ 103#Academic
In addition, from these two last correspondences, we could easily deduce the two first
ones.
Our suggestion for designing a better oriented matching track would be to remove
some classes and properties in the target ontologies so as to obtain complete reference
alignments with some subsumption relations. For example, it would be more accurate
to remove the concept MasterThesis from the ontology 103 in order to naturally change
101#MastersThesis ≡103#MastersThesis by 101#MastersThesis ≤ 103#Academic in
the reference alignment.
4 Conclusion
The version of AROMA includes a new matcher based on annotation equality. This
change allows better time efficiency because it reduces the number of unaligned enti-
ties before the use of a more time consuming terminological matcher. Furthermore, we
obtained better results on the 3xx tests of benchmark and tend to enhance the recall
obtained on anatomy track.
References
[Agrawal et al., 1993] Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining associa-
tion rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD
International Conference on Management of Data, pages 207–216. ACM Press, 1993.
[David and Euzenat, 2008] Jérôme David and Jérôme Euzenat. Comparison between ontology
distances (preliminary results). In Proceedings of the 7th International Semantic Web Confer-
ence, volume 5318 of Lecture Notes in Computer Science, pages 245–260. Springer, 2008.
[David et al., 2007] Jérôme David, Fabrice Guillet, and Henri Briand. Association rule ontology
matching approach. International Journal on Semantic Web and Information Systems, 3(2):27–
49, 2007.
[David, 2007] Jérôme David. AROMA : une méthode pour la découverte d’alignements orientés
entre ontologies à partir de règles d’association. PhD thesis, Université de Nantes, 2007.
[Gras et al., 2008] Régis Gras, Einoshin Suzuki, Fabrice Guillet, and Filippo Spagnolo, editors.
Statistical Implicative Analysis, Theory and Applications, volume 127 of Studies in Computa-
tional Intelligence. Springer, 2008.