Exploiting Majority Acceptable Arguments in Composite Ontology Matching
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International Journal of Artificial Intelligence
Abstract
Ontology matching consists of generating an alignment (set of correspondences) from
a pair of ontologies. This process has been seen as a mainstream solution to the semantic
heterogeneity problem in ontology-based systems. A wide diversity of matching solutions
has been proposed, which exploit different features within an ontology. Matching systems
usually differ in their results and an important issue is to combine different matching results
and deal with potential conflicts that arise from the different views. Our approach exploits
argumentation theory as a way for dealing with that issue. Here, arguments are as positions
that support or reject correspondences and argumentation frameworks support the creation
and exchange of arguments, followed by the reasoning on their acceptability. First, match-
ers generate their correspondences and represent them as arguments. Next, they share
their arguments and interpret them on the basis of argumentation frameworks and individ-
ual preferences. As a result, each matcher has a subset of acceptable arguments, from the
set of arguments initially shared. The subset of globally acceptable arguments (consensus)
is computed from the individual. In this paper, we exploit the notion of majority, where argu-
ments being acceptable by the majority of matchers are considered as a consensus on the
initial alignments. We evaluate our proposal on a standard set of alignments. Considering
the correspondences represented as arguments acceptable for the majority of individual
subsets, both precision and recall are improved, specially when compared with the subsets
acceptable for every matcher or for some matchers.