Morph-KGC: Scalable knowledge graph materialization with mapping partitions

Knowledge graphs are often constructed from heterogeneous data sources, using declarative rules that map them to a target ontology and materializing into RDF. When these sources large, the materialization of entire knowledge graph may be computationally expensive not suitable for those cases where rapid is required. In this work, we propose an approach overcome limitation, based on novel concept mapping partitions. Mapping partitions defined as groups generate disjoint subsets graph. Each can processed separately, reducing total amount memory execution time required by process. We have included optimization in our engine Morph-KGC, evaluated it over three different benchmarks. Our experimental results show that, compared with state-of-the-art techniques, use Morph-KGC presents following advantages: (i) decreases significantly materialization, (ii) reduces maximum peak used, (iii) scales sizes other engines capable processing currently.