RDF : An alternative approach for representing, storing, and maintaining meta-knowledge in web of data

Sen, Sangeeta; Katoriya, Devashish; Dutta, Animesh; Dutta, Biswanath

doi:10.1016/j.eswa.2021.115043

Cited by 6 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because GraphDB makes the assumption that there will be only a small number of properties in the graph. This issue is described on the website of GraphDB 18 . Details on how exactly the experiments were run are given at the end of this section.…”

Section: Methodsmentioning

confidence: 99%

Evaluating Reification with Multi-Valued Properties in a Knowledge Graph of Licensed Educational Resources

Kieffer,

Fakih,

Serrano Alvarado

2023

Studies on the Semantic Web

View full text Add to dashboard Cite

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 construction using standard semantic web technologies.

show abstract

Section: Methodsmentioning

confidence: 99%

Evaluating Reification with Multi-Valued Properties in a Knowledge Graph of Licensed Educational Resources

Kieffer,

Fakih,

Serrano Alvarado

2023

Studies on the Semantic Web

View full text Add to dashboard Cite

show abstract

“…Meta knowledge [34], also known as reification or statement-level metadata, allows describing RDF triples, for instance by providing provenance or certainty information for them. A large amount of meta knowledge alternatives has emerged since the inception of RDF.…”

Section: Meta Knowledgementioning

confidence: 99%

“…Recently, meta knowledge [34] has gained attention in the Semantic Web community. Meta knowledge allows describing (or annotating) RDF triples with information such as provenance or certainty.…”

Section: Introductionmentioning

confidence: 99%

“…Meta knowledge allows describing (or annotating) RDF triples with information such as provenance or certainty. Different approaches have been proposed [16,20,28,29,33,34], even extensions of RDF and SPARQL (RDF-star and SPARQL-star [19]) to facilitate the annotation of RDF triples. This growing interest in statement-level metadata is observed in the recent creation of an RDF-star Working Group 1 and the number of systems that have adopted RDF-star and SPARQL-star 2 , including several triplestores and two OBDA systems, Ontop [38] for virtualization, and Morph-KGC [3] for materialization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

Arenas-Guerrero,

Pérez,

Corcho

2024

JWE

View full text Add to dashboard Cite

Ontology-based data access focuses on enabling query evaluation over heterogeneous relational databases according to the model represented by an ontology. The relationships between the ontology and the data sources are commonly defined with declarative mappings, which are used by systems to perform SPARQL-to-SQL query translation or to generate RDF dumps from the relational databases. Besides the potential homogenization of data because of using an ontology, some additional advantages of this paradigm are that it may allow applying reasoning thanks to the ontology, as well as querying for meta knowledge, which describes statements with information such as provenance or certainty. In this paper, (i) we adapt a widely used RDF graph store benchmark, namely LUBM, for ontology-based data access, (ii) extend the benchmark for the evaluation of queries that exploit meta knowledge, and (iii) apply it for performance evaluation of state-of-the-art declarative mapping systems. Our proposal, the LUBM4OBDA Benchmark, considers inference capabilities that are not covered by previous ontology-based data access benchmarks, and it is the first one for the evaluation of meta knowledge and the RDF-star data model. The experimental evaluation shows that current virtualization systems cannot handle some advanced inference tasks, and that optimizations are needed to scale RDF-star materialization.

show abstract

A survey on advancements in image–text multimodal models: From general techniques to biomedical implementations

Guo,

Wei,

Sun

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

RDF : An alternative approach for representing, storing, and maintaining meta-knowledge in web of data

Cited by 6 publications

References 37 publications

Evaluating Reification with Multi-Valued Properties in a Knowledge Graph of Licensed Educational Resources

Evaluating Reification with Multi-Valued Properties in a Knowledge Graph of Licensed Educational Resources

LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

A survey on advancements in image–text multimodal models: From general techniques to biomedical implementations

Contact Info

Product

Resources

About