TY - JOUR

T1 - Scaling up knowledge graph creation to large and heterogeneous data sources

AU - Iglesias, Enrique

AU - Jozashoori, Samaneh

AU - Vidal, Maria Esther

N1 - Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Samaneh Jozashoori reports financial support was provided by European Commission. Maria-Esther Vidal reports financial support was partially provided by the Leibniz Association, Germany. Enrique Iglesias reports financial support was provided by Federal Ministry for Economic Affairs and Energy of Germany. Funding Information: This work has been partially supported by the EU H2020 RIA funded project CLARIFY with grant agreement No 875160 , EU H2020 project PLATOON with grant agreement No, 872592 , and by the Federal Ministry for Economic Affairs and Energy of Germany in the project CoyPu (project number 01MK21007[A-L]), Germany. Furthermore, Maria-Esther Vidal is partially supported by the Leibniz Association in the program “Leibniz Best Minds: Programme for Women Professors”, project TrustKG-Transforming Data in Trustable Insights with grant P99/2020 , Germany.

PY - 2023/1

Y1 - 2023/1

N2 - RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.

AB - RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.

KW - Data integration systems

KW - Knowledge graph creation

KW - Query execution planning

KW - RDF Mapping Languages

UR - http://www.scopus.com/inward/record.url?scp=85140098490&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2201.09694

DO - 10.48550/arXiv.2201.09694

M3 - Article

AN - SCOPUS:85140098490

VL - 75

JO - Journal of Web Semantics

JF - Journal of Web Semantics

SN - 1570-8268

M1 - 100755

ER -