MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Jozashoori, Samaneh; Vidal, María-Esther

doi:10.1007/978-3-030-33246-4_4

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…The work presented in [25] exploits knowledge encoded in the mapping documents to project the attributes appearing in a triples map, reducing the size of the data sources that need to be processed. Similarly, to diminish the impact of duplicates in the evaluation of join conditions, it also pushes down projections into joins.…”

Section: Related Workmentioning

confidence: 99%

Morph-KGC: Scalable knowledge graph materialization with mapping partitions

Arenas-Guerrero

Chaves-Fraga

Toledo

et al. 2024

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

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Section: Related Workmentioning

confidence: 99%

Morph-KGC: Scalable knowledge graph materialization with mapping partitions

Arenas-Guerrero

Chaves-Fraga

Toledo

et al. 2024

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“…RDF materialisation is an approach widely used to generate the RDF data of a KG from a set of heterogeneous sources that counts with a large number of existing tools [22,39,40,46,50,73]. Although some of these may differ on efficiency or suitability when applied in certain contexts, in general, they have the same workflow.…”

Section: Knowledge Graph Creationmentioning

confidence: 99%

Helio: A framework for implementing the life cycle of knowledge graphs

Cimmino

García‐Castro

2024

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Building and publishing knowledge graphs (KG) as Linked Data, either on the Web or in private companies, has become a relevant and crucial process in many domains. This process requires that users perform a wide number of tasks conforming to the life cycle of a KG, and these tasks usually involve different unrelated research topics, such as RDF materialisation or link discovery. There is already a large corpus of tools and methods designed to perform these tasks; however, the lack of one tool that gathers them all leads practitioners to develop ad-hoc pipelines that are not generic and, thus, non-reusable. As a result, building and publishing a KG is becoming a complex and resource-consuming process. In this paper, a generic framework called Helio is presented. The framework aims to cover a set of requirements elicited from the KG life cycle and provide a tool capable of performing the different tasks required to build and publish KGs. As a result, Helio aims at providing users with the means for reducing the effort required to perform this process and, also, Helio aims to prevent the development of ad-hoc pipelines. Furthermore, the Helio framework has been applied in many different contexts, from European projects to research work.

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“…In order to scale up the process of transforming data into RDF and creation of KG for big or complex data integration systems, different optimization frameworks are proposed; some of which can be applied along with mentioned tools. For instance, Szekely et al propose the DIG system [27] or Jozashoori and Vidal define MapSDI [18]) while Gawriljuk et al [13] present a scalable framework for incremental KG creation. Despite the significance of all mentioned contributions and improvements, these approaches do not address the problem of generating execution plans for mapping assertions.…”

Section: Mapping Languages and Kg Creation Frameworkmentioning

confidence: 99%

Scaling Up Knowledge Graph Creation to Large and Heterogeneous Data Sources

Iglesias¹,

Jozashoori²,

Vidal³

2022

Preprint

Self Cite

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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 data sources. As a result, engines that usually time in complex cases out can, if not entirely execute all the assertions, still produce a portion of the KG.

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MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Cited by 13 publications

References 9 publications

Morph-KGC: Scalable knowledge graph materialization with mapping partitions

Morph-KGC: Scalable knowledge graph materialization with mapping partitions

Helio: A framework for implementing the life cycle of knowledge graphs

Scaling Up Knowledge Graph Creation to Large and Heterogeneous Data Sources

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