=Paper=
{{Paper
|id=Vol-1111/om2013_poster3
|storemode=property
|title=Mix'n'Match: iteratively combining ontology matchers in an anytime fashion
|pdfUrl=https://ceur-ws.org/Vol-1111/om2013_poster3.pdf
|volume=Vol-1111
|dblpUrl=https://dblp.org/rec/conf/semweb/SteyskalP13
}}
==Mix'n'Match: iteratively combining ontology matchers in an anytime fashion==
<pdf width="1500px">https://ceur-ws.org/Vol-1111/om2013_poster3.pdf</pdf>
<pre>
    Mix’n’Match: Iteratively Combining Ontology
          Matchers in an Anytime Fashion

                     Simon Steyskal1,2 and Axel Polleres3,1
              1
               Siemens AG, Siemensstrasse 90, 1210 Vienna, Austria
             2
               Vienna University of Technology, 1040 Vienna, Austria
        3
          Vienna University of Economics & Business, 1020 Vienna, Austria



1    The Mix’n’Match Framework
Mix’n’Match is a framework to combine different ontology matchers in an
iterative fashion for improved combined results: starting from an empty set of
alignments, we aim at iteratively supporting in each round, matchers with the
combined results of other matchers found in previous rounds, aggregating the
results of a heterogeneous set of ontology matchers, cf. Fig.1.




                        Fig. 1. Framework of Mix’n’Match

    Alignment Combination: The combination of the alignments, especially
the choice of those which are used for the enrichment step is based on majority
votes. By only accepting alignments which were found by a majority of heteroge-
neous matching tools we aim to ensure a high precision of the found alignments
and therefore try to emulate reference alignments as e.g. provided by iterative
approval through a human domain expert. Although Mix’n’Match would support
the definition of an alignment confidence threshold as additional parameter (i.e,
only allowing alignments over a specific threshold to pass) we set this threshold
per default to 0 in our experiments: since the calculation of confidence values
is not standardized across matchers and some matchers only produce boolean
confidence values, e.g. [3]). Other result aggregation methods may be conceivable
here, like taking the individual performance of off-the-shelf matchers on specific
matching tasks into account [2, 4], but since this approach would lead to a more
inflexible alignment process this issue needs more detailed investigations in future
versions of Mix’n’Match.
    Ontology Enrichment: After mixing of the alignments, enrichment of the
ontologies takes place; since most ontology matchers do not support refernce
alignments (as specified by the OAEI alignment format4 ), we implement enrich-
ment by simple URI replacement to emulate such reference alignments found in
each matching round: for every pair of matched entities in the set of aggregated
alignments, a merged entity URI is created and will replace every occurrence
of the matched entities in both ontologies. This approach is motivated by the
assumption that if two entities were stated as equal by the majority of ontology
matchers, their URI can be replaced by an unified URI, stating them as equal
in the sense of URIs as global identifiers. Note that, despite the fact that most
matchers seem to ignore URIs as unique identifiers of entities, our experiments
showed that URI replacement was effective in boosting the confidence value of
such asserted alignments in almost all considered matchers.
    Intermediate Results and Anytime Behavior: We collect the interme-
diate results of every finished off-the-shelf matcher in every iteration. Furthermore
we keep track of every alignment found so far together with the number of in-
dividual matchers which have found this alignment in any previous matching
round. This offers the possibility to interrupt the matching process at any time,
retrieving only those alignments which have been found by the majority of the
ontology matchers at the time the interruption has taken place. In contrast to
other ontology matchers which offer this anytime behavior like MapPSO [1], we
are not only restricted to gather alignment results of the last finished matching
iteration, but also use the alignment results of already finished off-the-shelf
matchers in the current matching round.
    Evaluation Results To test our approach, we based our evaluations on
OAEI evaluation tracks (Benchmark, Conference, Anatomy) and retrieved very
promising results, typically outperforming the single matchers combined within
the Mix’n’Match framework in terms of F-measure. For detailed evaluation results
we refer our readers to an extended report accompanying this poster, available
at http://www.steyskal.info/om2013/extendedversion.pdf.

References
 1. J. Bock, J. Hettenhausen. Discrete particle swarm optimisation for ontology
    alignment. Information Sciences, 192:152–173, 2012.
 2. I.F. Cruz, F. Palandri Antonelli, C. Stroe. Efficient selection of mappings and
    automatic quality-driven combination of matching methods. In Int’l Workshop on
    Ontology Matching (OM), CEUR volume 551, pages 49–60. Citeseer, 2009.
 3. M. Seddiqui Hanif, M. Aono. An efficient and scalable algorithm for segmented
    alignment of ontologies of arbitrary size. J. Web Sem., 7(4):344–356, 2009.
 4. A. Nikolov, M. d’Aquin, E. Motta. Unsupervised learning of link discovery configu-
    ration. In ESWC2012, pages 119–133. Springer, 2012.




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    http://alignapi.gforge.inria.fr/format.html

</pre>