Computing minimal mappings between lightweight ontologies

Abstract

Abstract. As a valid solution to the semantic heterogeneity problem, many matching solutions have been proposed. Given two lightweight ontologies, we compute the minimal mapping, namely the subset of all possible correspondences, that we call mapping elements, between them such that i) all the others can be computed from them in time linear in the size of the input ontologies, and ii) none of them can be dropped without losing property i). We provide a formal definition of minimal mappings and define a time efficient computation algorithm which minimizes the number of comparisons between the nodes of the two input ontologies. The experimental results show a substantial improvement both in the computation time and in the number of mapping elements which need to be handled, for instance for validation, navigation and search.

Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

“…In the future editions of the Disease and Phenotype track, apart from including new datasets and updated versions, we aim to enhance the evaluation in a number of ways. We will consider new metrics like the mapping incoherence [ 12 ], the functional coherence [ 42 ] or the redundancy (minimality) [ 43 ] to evaluate the computed alignments. We also intend to redefine the notion of semantic precision and recall , using the using the semantic closure of the (aligned) ontologies, in order to include the cases where the aligned ontology is incoherence (i.e., contains unsatisfiable classes).…”

A semantic interoperability approach to health‐care data: Resolving data‐level conflicts

“…The study of Giunchiglia et al . () presented the structural conflicts resolution (equivalence, generalization, and disjointness) between heterogeneous ontologies, aiming at reducing the number of mapping rules for the minimalistic mappings. Several studies focused on applying ontology mapping techniques to enable the interoperability of services and applications (Panetto et al , ; Zheng & Terpenny, ).…”

“…As surveyed in studies by Choi et al (2006), Euzenat et al (2013), and Shvaiko and Euzenat (2013), many studies have focused on ontology matching techniques to solve the semantic heterogeneity of ontologies by creating a semi-automatic approach to ontology merging and alignment without dealing with data integration or transformation (Hu et al, 2008;Juanzi et al, 2009;Jean-Mary et al, 2009;Fürst & Trichet, 2009;Wang et al, 2013). The study of Giunchiglia et al (2012) presented the structural conflicts resolution (equivalence, generalization, and disjointness) between heterogeneous ontologies, aiming at reducing the number of mapping rules for the minimalistic mappings. Several studies focused on applying ontology mapping techniques to enable the interoperability of services and applications (Panetto et al, 2012;Zheng & Terpenny, 2013).…”

A method of conflict checking for ontology integration based on distributed interpretation

“…Giuchiglia [12] computes minimal mappings on the all possible correspondences. They define several relations of mappings and identify four redundancy pattern.…”