Similarity-Based Ontology Alignment in OWL-Lite

By Jérôme Euzenat, Petko Valtchev. European Conference on Artificial Intelligence, 2004

468 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b96fe46b358d700c4c0547e6ac7e899a8a22fb13

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Citations in the corpus

Semantic integration: a survey of ontology-based approaches (2004, unknown): “For example, a similarity metric between concepts in OWL ontologies developed by Euzenat and Volchev [7] is a weighted combination of similarities of various features in OWL concept de.nitions: their labels, domains and ranges of properties, restrictions on properties (such as cardinality restri”
A Survey of Schema-Based Matching Approaches (2005, unknown): “By a mapping query we mean here the pair of nodes and a possible relation between them.”
GMO: A Graph Matching for Ontologies (2005, background): “OLA [4] and ASCO [8] are dedicated to the alignment of OWL ontologies (with an emphasis on OWL-Lite), and try to use as much as possible all of the information extracted from two given ontologies.”
Ontology matching: A literature review (2015, methodology): “This system was developed between 2004 (Euzenat & Valtchev, 2004) and 2005 (Euzenat, Guégan, & Valtchev, 2005 ), later, it was revisited in 2007 (Kengue et al., 2007 ).”
Ontology Matching (2007, methodology): “OLA (OWL Lite Aligner) (Euzenat and Valtchev 2004) is an ontology matching”
Semantic Matching: Algorithms and Implementation (2007, background): “Many diverse solutions of match have been proposed so far, see [43,12,40,42] for recent surveys, while some examples of individual approaches addressing the matching problem can be found in [1,2,5,6,10,11,13,16,30,32,33,35,39]1.”
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (2009, background): “LI ET AL.: RIMOM: A DYNAMIC MULTISTRATEGY ONTOLOGY ALIGNMENT FRAMEWORK 1229 instances, etc.) [ 17 ].”
Ontology Matching with Semantic Verification (2009, unknown): “The nature of this calculation process is similar to the approach used in OLA [15], both in its use of a normalized weighted average of multiple similarities along different ontology facets, and especially in its use of similarities between entity sets.”
Ontology Matching: State of the Art and Future Challenges (2013, result): “It equals to that of Prior+ [62] and is still lower than the best F-measure of 2007 (0.71) by OLA 2 [63].”