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In recent years, an increasing number of knowledge bases have been built using linked data, thus datasets have grown substantially. It is neither reasonable to store a large amount of triple data in a single graph, nor appropriate to store RDF in named graphs by class URIs, because many joins can cause performance problems between graphs. This paper presents an agglomerative-adapted approach for large-scale graphs, which is also a bottom-up merging process. The proposed algorithm can partitiondoi:10.23974/ijol.2019.vol4.1.106 fatcat:lxhzzyvterbrxajff65br3vz2e