LogMap: Logic-Based and Scalable Ontology Matching

By Ernesto Jiménez-Ruiz, Bernardo Cuenca Grau. International Workshop on the Semantic Web, 2011

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Ontology Matching: State of the Art and Future Challenges (2013, methodology): “approach [103] uses an incomplete reasoner as well as a”
Domain-specific Knowledge Graphs: A survey (2020, methodology): “The construction of TERA KG was carried out by using LogMap ontology alignment system [128] to index and align the ECOTOX and NCBI vocabularies.”
Ontology matching: A literature review (2015, background): “…a high level of maturity as in the last 3 years there have been at least 6 publication describing its performance and results in the OAEI contests (Jiménez-Ruiz & Cuenca Grau, 2011; Jiménez-Ruiz, Grau, & Zhou, 2011; Jiménez-Ruiz, Morant, & Grau, 2011; Jiménez-Ruiz, Grau, & Horrocks, 2012;…”
Results of the Ontology Alignment Evaluation Initiative 2014 (2014, methodology): “We encourage ontology matching system developers to develop their own repair techniques or to use state-of-the-art techniques such as Alcomo [23], the repair module of LogMap (LogMap-Repair) [17] or the repair module of AML [29], which have shown to work well in practice [19, 16].”
A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, neutral): “ed in [1, 62]. 2.1.2 Conventional Entity Alignment Approaches. Conventional approaches address entity alignment mainly from two angles. One is based on equivalence reasoning mandated by OWL semantics [22, 34]. The other is based on similarity computation, which compares symbolic features of entities”