What Are the Parameters that Affect the Construction of a Knowledge Graph?

Chaves-Fraga, David; Endris, Kemele M.; Iglesias, Enrique; Corcho, Óscar; Vidal, María-Esther

doi:10.1007/978-3-030-33246-4_43

Cited by 17 publications

(35 citation statements)

References 16 publications

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“…Ganzha et al (2018) presented an mechanism to identifier management and potential ID interoperability architectures built as part of the INTER-IoT project. Chaves-Fraga et al (2019b) contributed an empirical configuration which can be reemployed for the assessment of knowledge graph development tools and mapping languages (e.g. SPARQL-Generate, TARQL or R2RML).…”

Section: Related Workmentioning

confidence: 99%

RML based ontology development approach in internet of things for healthcare domain

Ahamed

Mir

Chishti

2021

IJPCC

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Purpose A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain. Design/methodology/approach To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used. Findings Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data. Originality/value The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

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Section: Related Workmentioning

confidence: 99%

RML based ontology development approach in internet of things for healthcare domain

Ahamed

Mir

Chishti

2021

IJPCC

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“…Chaves-Fraga et al [4] empirically analyze the parameters affecting the performance of an RML interpreter. Five dimensions are defined considering different complexity variables, i.e., mapping, data, platform, source, and output.…”

Section: Parameters Affecting An Rml Interpretermentioning

confidence: 99%

“…Finally, the output dimension defines three variables, i.e., the serialization type of the output, whether the interpreter removes duplicates or not, and the generation type generating the output at once or in a streaming manner. From an interpreter perspective, Chaves-Fraga et al [4] show that the dimensions of mapping complexity, data, and output are the ones that impact the most. Efficient join operators are of paramount importance for an RML interpreter efficiency during execution time.…”

Section: Parameters Affecting An Rml Interpretermentioning

confidence: 99%

“…Diverse approaches have been proposed to define the process of integrating heterogeneous datasets into knowledge graphs [3][4][5]11]. Mapping languages (e.g., R2RML [7] and RML [8]) and engines (e.g., RMLMapper 3 and RocketRML [12]) represent valuable contributions for performing this transformation process.…”

Section: Introductionmentioning

confidence: 99%

“…RBP_RNA_PhysicalInteraction 4 from different sources into RDF. Accordingly, the subject of TripleMap1 represents the mentioned concept.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SDM-RDFizer

Iglesias

Jozashoori

Chaves-Fraga

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.

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Balancing RDF Generation from Heterogeneous Data Sources

Assche

2022

The Semantic Web: ESWC 2022 Satellite Events

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Knowledge graphs in RDF are often generated from heterogeneous data sources to power services. However, knowledge graph generation is an unbalanced effort for producers compared to consumers of a knowledge graph. In this paper, I present my research about (i) investigating current RDF knowledge graph production and consumption approaches, and (ii) how to involve the consumer into a hybrid RDF generation approach to reduce the necessary resources for generating RDF for producers & consumers. I discuss the shortcomings of existing approaches for RDF generation from heterogeneous data sources (i.e., materialization and virtualization) and how I will address these: a Systematic Literature Review; an analysis and a set of guidelines for producers to select the right approach for an use case; and a combined hybrid approach to balance the producer's and consumer's effort in RDF generation. I already performed a Systematic Literature Review to get an overview of the existing approaches for RDF production from heterogeneous data sources. These results will be used to establish a set of producer guidelines, a benchmark to compare the current materialization and virtualization approaches, and evaluate the proposed hybrid approach. Thanks to my research, knowledge graph production and consumption will be more balanced and accessible to smaller companies and individuals. This way, they can focus on providing better services on top of a knowledge graph instead of being limited by the lack of computing resources to harvest enormous amounts of data from the Web and integrate it into a knowledge graph.

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What Are the Parameters that Affect the Construction of a Knowledge Graph?

Cited by 17 publications

References 16 publications

RML based ontology development approach in internet of things for healthcare domain

RML based ontology development approach in internet of things for healthcare domain

SDM-RDFizer

Balancing RDF Generation from Heterogeneous Data Sources

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