{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intermediate-triple-table-a-general","title":"Intermediate triple table: A general architecture for virtual knowledge graphs","arxiv_id":null,"date":"2025-02-24","proceeding":"Knowledge-Based Systems 2025 2","authors":["Julián Arenas-Guerrero","Oscar Corcho","María S. Pérez"],"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 for a 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.","url_abs":"https://doi.org/10.1016/j.knosys.2025.113179","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0950705125002266/pdfft?md5=5d4be06253edcd662cff2fff45c40f86&pid=1-s2.0-S0950705125002266-main.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intermediate-triple-table-a-general","repo_url":"https://github.com/arenas-guerrero-julian/ITT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"}],"methods":[{"method_slug":"focus","method_name":"Focus"},{"method_slug":"ontology","method_name":"Ontology"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}