E
Ernesto Jiménez-Ruiz
Researcher at City University London
Publications - 191
Citations - 4857
Ernesto Jiménez-Ruiz is an academic researcher from City University London. The author has contributed to research in topics: Ontology (information science) & Ontology alignment. The author has an hindex of 32, co-authored 183 publications receiving 4233 citations. Previous affiliations of Ernesto Jiménez-Ruiz include James I University & University of Oslo.
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Proceedings Article
Results of the ontology alignment evaluation initiative 2012
José Luis Aguirre,Kai Eckert,Jérôme Euzenat,Alfio Ferrara,Willem Robert van Hage,Laura Hollink,Christian Meilicke,Andriy Nikolov,Dominique Ritze,François Scharffe,Pavel Shvaiko,Ondřej Šváb-Zamazal,Cassia Trojahn,Ernesto Jiménez-Ruiz,Bernardo Cuenca Grau,Benjamin Zapilko +15 more
TL;DR: The OAEI 2012 campaign on ontology matching as mentioned in this paper was the first one to use a new evaluation modality which provides more automation to the evaluation of ontologies, e.g., blind evaluation, open evaluation, consensus.
Book ChapterDOI
LogMap: logic-based and scalable ontology matching
TL;DR: This paper presents LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds of thousands of classes).
Proceedings ArticleDOI
Large-scale interactive ontology matching: algorithms and implementation
TL;DR: This paper presents the ontology matching system LogMap 2, a much improved version of its predecessor LogMap, which supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings.
Book ChapterDOI
Ontology Integration Using Mappings: Towards Getting the Right Logical Consequences
TL;DR: A general method and novel algorithmic techniques to facilitate the integration of independently developed ontologies using mappings and a preliminary evaluation suggests that this approach is both useful and feasible in practice.
Journal ArticleDOI
Assessment of disease named entity recognition on a corpus of annotated sentences
Antonio Jimeno,Ernesto Jiménez-Ruiz,Vivian Lee,Sylvain Gaudan,Rafael Berlanga,Dietrich Rebholz-Schuhmann +5 more
TL;DR: MetaMap generates precise results at the expense of insufficient recall while the statistical method obtains better recall at a lower precision rate, and dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature.