=Paper=
{{Paper
|id=Vol-2032/om2017_poster6
|storemode=property
|title=Towards a complex alignment evaluation dataset
|pdfUrl=https://ceur-ws.org/Vol-2032/om2017_poster6.pdf
|volume=Vol-2032
|authors=Élodie Thiéblin,Ollivier Haemmerlé,Nathalie Hernandez,Cassia Trojahn
|dblpUrl=https://dblp.org/rec/conf/semweb/ThieblinHHS17
}}
==Towards a complex alignment evaluation dataset==
https://ceur-ws.org/Vol-2032/om2017_poster6.pdf
Towards a complex alignment evaluation dataset
Élodie Thiéblin, Ollivier Haemmerlé, Nathalie Hernandez, Cassia Trojahn
IRIT & Université de Toulouse 2 Jean Jaurès, Toulouse, France
{firstname.lastname}@irit.fr
Keywords: complex alignments, evaluation dataset, complex dataset
1 Motivation and background
Simple ontology alignments, largely studied, link one entity from a source ontol-
ogy to one entity of a target ontology. One of the limitations of these alignments
is, however, their lack of expressiveness which can be overcome by complex align-
ments. Different approaches for generating complex alignments have emerged in
the literature [4,5,6]. However, there is a lack of datasets on which they can be
evaluated.
Ontology matching is the process of generating an alignment. An alignment
A between a source o1 and a target o2 ontologies is a set of correspondences [2].
Each correspondence is a triple heo1 , eo2 , ri. eo1 and eo2 are the members of the
correspondence: they can be single ontology entities or constructions of these
entities using constructors or transformation functions. r is a relation (e.g., ≡,
≤, ≥) between eo1 and eo2 . We consider two types of correspondences:
– simple correspondence when both eo1 and eo2 are single entities: e.g. ∀x,
o1:Person(x) ≡ o2:Human(x) is a simple correspondence.
– complex correspondence when at least one of eo1 or eo2 is a construction of
entities, i.e. involving at least a constructor or a transformation function. For
example, ∀x,y, o1:priceInDollars(x,y) ≡ ∃y1, o2:priceInEuro(x,conversion(y))
is a complex correspondence with a transformation function (conversion
that states that y1 = changeRate × y). ∀x, o1:AcceptedPaper(x) ≡ ∃y,
o2:Paper(x) ∧ o2:acceptedBy(x,y) is a complex correspondence with con-
structors.
A complex alignment contains at least one complex correspondence.
2 The evaluation dataset
The proposed dataset is based on the OntoFarm dataset [9] composed of 16 on-
tologies on the conference organisation domain and simple reference alignments
between 7 of these ontologies. This dataset has been widely used in the ontology
alignment evaluation domain [8]. The dataset proposed here is a first version of
an extension of the OntoFarm dataset including complex correspondences. 3 out
of the 7 ontologies of the reference alignments have been manually aligned (cmt,
conference and edas), resulting in 3 alignments: cmt-conference, cmt-edas and
conference-edas. The methodology applied to create the complex dataset consists
in manually finding an equivalent construction of target entities for each source
entity. All correspondences have a single entity member and an other member
that is either a single entity (simple correspondence) or a construction (complex
correspondence). The correspondences are diverse for they can be classified with
8 different correspondence patterns or compositions of them [7]. In the 3 align-
ments, the dataset contains 51 complex correspondences. The alignments are
expressed in First Order Logic and in EDOAL1 . The resulting alignments were
translated into OWL axioms as an ontology merging process. The HermiT rea-
soner [3] was used to check the consistency of the merged ontology. The dataset is
available online at http://doi.org/10.6084/m9.figshare.4986368.v4 under
a CC-BY License.
3 Conclusion and future work
We have proposed a complex coherent dataset with complex correspondences
between 3 ontologies of the OntoFarm dataset. As perspectives, the dataset will
be extended with other ontologies of this dataset. The confidence of a correspon-
dence (a value associated with a correspondence to express its confidence degree)
could be added to the dataset. This could express, as in [1], the consensus level
of experts on each correspondence. Finally, we aim at using this dataset for the
purpose of evaluating complex matchers.
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1
http://alignapi.gforge.inria.fr/edoal.html