LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

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...

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Bibliographic Details
Published in:Journal of web engineering 2023-01, Vol.22 (8), p.1163-1186
Main Authors: Arenas-Guerrero, Julián, Pérez, María S., Corcho, Oscar
Format: Article
Language:English
Subjects:
Benchmarks
Datasets
Inference
Knowledge
Metadata
Ontology
Performance evaluation
Queries
Relational data bases
Semantic web
Semantics
State-of-the-art reviews
Virtual communities
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Summary: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.
ISSN:1540-9589
1544-5976
DOI:10.13052/jwe1540-9589.2284