Human-Friendly RDF Graph Construction: Which One Do You Chose?

Iglesias-Molina, Ana; Chaves-Fraga, David; Dasoulas, Ioannis; Dimou, Anastasia

doi:10.1007/978-3-031-34444-2_19

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Our future work includes implementing the remaining modules of the new RML specification, and providing support to user-friendly mappings (YARRRML [25,27]). We also plan to improve the performance of RDF-star materialization, e.g., by extending mapping partitioning [4] to RML-star.…”

Section: Discussionmentioning

confidence: 99%

“…Lines 22-25 of Algorithm 1 process graph maps when nestLevel is 0 and a graph map is provided, generating quads. If the outermost mapping rule does not have a graph map, RDF-star triples are added to the default graph of the output dataset (lines [26][27]. When processing an inner mapping rule, the generated triples must be quoted (lines 29-30), i.e., enclosed with "«" and "»".…”

Section: Triples Mapsmentioning

confidence: 99%

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Declarative generation of RDF-star graphs from heterogeneous data

Arenas-Guerrero,

Iglesias-Molina,

Chaves-Fraga

et al. 2024

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RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.

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Section: Discussionmentioning

confidence: 99%

Section: Triples Mapsmentioning

confidence: 99%

Declarative generation of RDF-star graphs from heterogeneous data

Arenas-Guerrero,

Iglesias-Molina,

Chaves-Fraga

et al. 2024

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“…We leverage the RDF Mapping Language (RML) [12,30] to map the heterogeneous data (Pandas DataFrames from OpenML, CSV files from Kaggle and JSON files from Papers with Code) to RDF triples. To define the RML mapping rules, we leverage the human-friendly serialization YARRRML [26] and the Yatter [29] tool which converts YARRRML mapping rules to RML rules.…”

Section: Knowledge Graph Construction Processmentioning

confidence: 99%

MLSea: A Semantic Layer for Discoverable Machine Learning

Dasoulas,

Yang,

Dimou

2024

Lecture Notes in Computer Science

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With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously grow in numbers, complexity and components. Online platforms (e.g., OpenML, Kaggle) aim to gather and disseminate ML experiments. However, available knowledge is fragmented with each platform representing distinct components of the ML process or intersecting components but in different ways. To address this problem, we leverage semantic web technologies to model and integrate ML datasets, experiments, software and scientific works into MLSea, a resource consisting of: (i) MLSO, an ontology that models ML datasets, pipelines and implementations; (ii) MLST, taxonomies with collections of ML knowledge formulated as controlled vocabularies; and (iii) MLSea-KG, an RDF graph containing ML datasets, pipelines, implementations and scientific works from diverse sources. MLSea paves the way for improving the search, explainability and reproducibility of ML pipelines.

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