Construction of Knowledge Graphs: Current State and Challenges

Hofer, Marvin; Obraczka, Daniel; Saeedi, Alieh; Köpcke, Hanna; Rahm, Erhard

doi:10.2139/ssrn.4605059

Cited by 8 publications

(3 citation statements)

References 130 publications

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“…However, there are other solutions that adapt different languages (e.g., SPARQL) for integration purposes, such as SPARQL-Anything [5] and SPARQL-Generate [40]. The works by Hofer et al [31] and Van Assche et al [6] contribute to the understanding of the most suitable mapping languages, ontologies, data sources, and KG creation engines based on specific use cases. Hofer et al report a comprehensive study to determine the state-of-the-art in KG construction, defining the requirements for popular KGs like DBpedia and WorldKG, evaluating existing tools and strategies, and identifying areas that require further attention.…”

Section: Knowledge Graph Creation and Existing Technologiesmentioning

confidence: 99%

“…30 The engine is also available on the Python Package Index (PyPI), so it can be easily installed and integrated in other developments. 31 Finally, we also provide a docker image that deploys the SDM-RDFizer as a web service.…”

Section: Main Characteristics Of Sdm-rdfizermentioning

confidence: 99%

“…The DOI for the SDM-RDFizer repository is: https://doi.org/10.5281/zenodo.3872103 30. For example, the DOI for the v4.5.6 used in the experiments of this paper is: https://doi.org/10.5281/zenodo.7027549 31. https://pypi.org/project/rdfizer/.…”

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confidence: 99%

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Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

Iglesias,

Vidal,

Collarana

et al. 2024

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The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.

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