Information Retrieval
Semantic precision and recall for ontology alignment evaluation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Expressive fuzzy description logics over lattices
Knowledge-Based Systems
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
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To evaluate and compare various ontology mapping algorithms, it is indispensable to apply them to a group of test ontology pairs and compare the results (named mappings, also known as alignments) with some reference. Currently, the most widely used criteria are precision and recall. However, they do not always work well while facing objects with semantics, like ontologies and alignments, because they only consider ''exact'' correspondences but ignore ''close'' ones. Some new measures have been proposed to solve the problem. However, they have their own limitations, and do not rely purely on the semantics of ontologies and alignments. A framework for purely semantic precision and recall of ontology mapping is proposed in this paper, which can guarantee that the measures are purely semantic. Then a group of application-oriented measures are proposed to instantiate the framework. In some examples these measures are compared with former ones.