Evaluating Reification with Multi-valued Properties in a Knowledge Graph of Licensed Educational Resources
| Autor: | Kieffer, Manoé, Fakih, Ginwa, Serrano-Alvarado, Patricia |
|---|---|
| Přispěvatelé: | Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Gestion de Données Distribuées (LS2N - équipe GDD), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), LABEX Cominlabs - ANR-10-LABX-07-01, Projet Cominlabs CLARA |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Knowledge graph
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] RDF reification [INFO.INFO-WB]Computer Science [cs]/Web multi-valued properties query evaluation educational resources [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | SEMANTiCS SEMANTiCS, Sep 2023, Leipzig, Germany |
| Popis: | International audience; This paper presents the construction of a Knowledge Graph (KG) of Educational Resources (ER), where RDF reification is essential. The ERs are described based on the subjects they cover considering their relevance. RDF reification is used to incorporate this subject’s relevance. Multiple reification models with distinct syntax and performance implications for storage and query processing exist. This study aims to experimentally compare four statement-based reification models with four triplestores to determine the most pertinent choice for our KG. We built four versions of the KG. Each version has a distinct reification model, namely standard reification, singleton properties, named graphs, and RDF-star, which were obtained using RML mappings. Each of the four triplestores (Virtuoso, Jena, Oxigraph, and GraphDB) was setup four times (except for Virtuoso, which does not support RDF-star), and seven different SPARQL queries were experimentally evaluated. This study shows that standard reification and named graphs lead to good performance. It also shows that, in the particular context of the used KG, Virtuoso outperforms Jena, GraphDB, and Oxigraph in most queries. The recent specification of RDF-star and SPARQL-star sheds light on statement-level annotations. The empirical study reported in this paper contributes to the efforts towards the efficient usage of RDF reification. In addition, this paper shares the pipeline of the KG con- struction using standard semantic web technologies. |
| Databáze: | OpenAIRE |
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