Results 61 to 70 of about 201 (160)

Performance assessment of ontology matching systems for FAIR data. [PDF]

J Biomed Semantics, 2022
van Damme P, Fernández-Breis JT, Benis N, Miñarro-Gimenez JA, de Keizer NF, Cornet R.   +5 more
europepmc   +1 more source

POMap results for OAEI 2017

, 2017
Ontology matching is an effective strategy to find the correspondences among different ontologies in a scalable and heterogeneous semantic web. In order to find these correspondences, a matching system should be built aiming to ensure the interoperability between the aligned entities.
Laadhar, Amir, Ghozzi, Faiza, Megdiche, Imen, Ravat, Franck, Teste, Olivier, Gargouri, Faiez   +5 more
openaire   +2 more sources

DisMatch results for OAEI 2016

, 2016
DisMatch is an experimental ontology matching system based on the use of corpus based distributional measure for approximating se- mantic relatedness. Through the use of a domain-related corpus, the measure can be applied to a problem focused on the domain of the cor- pus, here being the Disease and Phenotype track.
Rybinski, Maciej, Roldán García, María del Mar, García Nieto, José Manuel, Aldana Montes, José F.   +3 more
openaire   +3 more sources

CroMatcher - results for OAEI 2013

, 2013
CroMatcher is an ontology matching system based on terminological and structural matchers. The most important part of the system is automated weighted aggregation of correspondences produced by using different basic ontology matchers. This is the first year CroMatcher has been involved in the OAEI campaign. The results obtained this year will certainly
Vrdoljak, Boris, Gulić, Marko
openaire   +2 more sources

SL-CMTGP: An Effective Knowledge Interaction Matching Model Between Multitasks for Large-Scale Biomedical Ontologies

Big Data Mining and Analytics
Biomedical ontologies encapsulate the vast knowledge within the medical domain, facilitating communication and data exchange. However, the heterogeneity of these ontologies often impedes knowledge exchange, especially in large-scale biomedical ontologies.
Donglei Sun, Qing Lv, Pei−Wei Tsai, Xingsi Xue, Kai Zhang, Ketao Dai   +5 more
doaj   +1 more source

DLinker Results for OAEI 2022

, 2022
DLinker is a system for matching instances of two RDF data sources. Its performance is mainly based on the deep comparison of literals. The main comparison algorithm is based on the search for the longest common subsequence (LCS) present in the literals. The validation of the similarity between two literals is performed by a mathematical formula.
Happi Happi, Bill Gates, Fokou Pelap, Géraud, Symeonidou, Danai, Larmande, Pierre   +3 more
openaire   +1 more source

Analysis and implementation of the DynDiff tool when comparing versions of ontology. [PDF]

J Biomed Semantics, 2023
Diaz Benavides S, Cardoso SD, Da Silveira M, Pruski C.   +3 more
europepmc   +1 more source

Matching Biomedical Ontologies via a Hybrid Graph Attention Network. [PDF]

Front Genet, 2022
Wang P, Hu Y.
europepmc   +1 more source

SMAT: An attention-based deep learning solution to the automation of schema matching. [PDF]

Adv Databases Inf Syst, 2021
Zhang J, Shin B, Choi JD, Ho JC.
europepmc   +1 more source

DKP-AOM: results for OAEI 2015

, 2015
In this paper, we present the results obtained by our DKP-AOM system within the OAEI 2015 campaign. DKP-AOM is an ontology merging tool designed to merge heterogeneous ontologies. In OAEI, we have participated with its ontology mapping component which serves as a basic module capable of matching large scale ontologies before their merging.
openaire   +2 more sources