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This work has received funding from the INESData project (Infrastructure to Investigate Data Spaces in Distributed Environments at UPM) , a project funded under the UNICO I+D CLOUD call by the Spanish Ministry for Digital Transformation and the Civil Service, within the framework of the recovery plan PRTR financed by the European Union (NextGenerationEU) .

Analysis of institutional authors

Arenas-Guerrero, JulianCorresponding Author
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Article

Intermediate triple table: A general architecture for virtual knowledge graphs

Publicated to:Knowledge-Based Systems. 314 113179- - 2025-04-08 314(), DOI: 10.1016/j.knosys.2025.113179

Authors: Arenas-Guerrero, Julian; Corcho, Oscar; Perez, Maria S

Affiliations

Univ Politecn Madrid, Madrid, Spain - Author

Abstract

Virtual knowledge graphs (VKGs) have been widely applied to access relational data with a semantic layer by using an ontology in use cases that are dynamic in nature. However, current VKG techniques focus mainly on accessing a single relational database and remain largely unstudied for data integration with several heterogeneous data sources. To overcome this limitation, we propose intermediate triple table (ITT), a general VKG architecture to access multiple and diverse data sources. Our proposal is based on data shipping and addresses heterogeneity by adopting a schema-oblivious graph representation that intervenes between the sources and the queries. We minimize data computation by just materializing a relevant subgraph fora specific query. We employ star-shaped query processing and extend this technique to mapping candidate selection. For rapid materialization of the ITT, we apply a mapping partitioning technique to parallelize mapping execution, which also guarantees duplicate-free subgraphs and reduces memory consumption. We use SPARQL-to-SQL query translation to homogeneously evaluate queries over the ITT and execute them with an in-process analytical store. We implemented ITT on top of a knowledge graph materialization engine and evaluated it with two VKG benchmarks. The experimental results show that our proposal outperforms state-of-the-art techniques for complex graph queries in terms of execution time. It also decreases the number of timeouts although it uses more memory as a trade-off. The experiments also demonstrate the source independence of the architecture on a mixed distribution of data with SQL and document stores together with various file formats.

Keywords
Data integrationData virtualizationDeclarative mappingGraph queryingQuerieSparql-to-sqlVirtual knowledge graph

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 27/197, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-21:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 3 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ARENAS GUERRERO, JULIAN) and Last Author (Perez, Maria S).

    the author responsible for correspondence tasks has been ARENAS GUERRERO, JULIAN.