research-article

Modeling and querying temporal RDF knowledge graphs with relational databases

Authors:

Nasrullah Khan,

Zongmin MaAuthors Info & Claims

Journal of Intelligent Information Systems, Volume 61, Issue 2

Pages 569 - 609

https://doi.org/10.1007/s10844-023-00780-6

Published: 30 March 2023 Publication History

Abstract

RDF (Resource Description Framework), a standard resource description model, is popularized and applied in many application scenarios for its explicit representation of semantics. To represent and process time-aware semantics with RDF, the temporal RDF model is proposed and applied in temporal knowledge graphs. The requirement for efficiently handling diverse temporal RDF data has become increasingly important with the rapid development and popularity of RDF. In this paper, we propose a novel temporal RDF model and effectively tackle the management of temporal RDF data in relational databases. In particular, we propose a temporal RDF model called tRDF to represent both temporal entities and relationships and further propose a temporal query language for the tRDF model. To manage temporal RDF data in an effective manner, we propose to store temporal RDF data with relational databases that follow the SQL:2011 standard and support temporal data manipulation. To query the tRDF data stored in relational databases with the tRDF query language, we implement the transformation from this query language to SQL. The experimental results show the feasibility and effectiveness of the proposed tRDF model as well as its storage and query methods.

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Cited By

Eremeev APaniavin NFomina M(2024)Representation and Processing of Temporal Cases in Real-Time Intelligent SystemsPattern Recognition and Image Analysis10.1134/S105466182470055X34:3(702-709)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1134/S105466182470055X
Zhang WYang C(2024)Relation representation based on private and shared features for adaptive few-shot link predictionJournal of Intelligent Information Systems10.1007/s10844-024-00856-x62:5(1375-1401)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10844-024-00856-x
Du CLi XLi Z(2024)Semantic-enhanced reasoning question answering over temporal knowledge graphsJournal of Intelligent Information Systems10.1007/s10844-024-00840-562:3(859-881)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00840-5
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cover image Journal of Intelligent Information Systems

Journal of Intelligent Information Systems Volume 61, Issue 2

Oct 2023

319 pages

ISSN:0925-9902

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Kluwer Academic Publishers

United States

Publication History

Published: 30 March 2023

Accepted: 30 January 2023

Revision received: 29 January 2023

Received: 04 November 2022

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Cited By

Eremeev APaniavin NFomina M(2024)Representation and Processing of Temporal Cases in Real-Time Intelligent SystemsPattern Recognition and Image Analysis10.1134/S105466182470055X34:3(702-709)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1134/S105466182470055X
Zhang WYang C(2024)Relation representation based on private and shared features for adaptive few-shot link predictionJournal of Intelligent Information Systems10.1007/s10844-024-00856-x62:5(1375-1401)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10844-024-00856-x
Du CLi XLi Z(2024)Semantic-enhanced reasoning question answering over temporal knowledge graphsJournal of Intelligent Information Systems10.1007/s10844-024-00840-562:3(859-881)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00840-5
Thambi SReghu Raj P(2024)A novel technique using graph neural networks and relevance scoring to improve the performance of knowledge graph-based question answering systemsJournal of Intelligent Information Systems10.1007/s10844-023-00839-462:3(809-832)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00839-4
Slifka JKnaisl VPergl R(2024)Evolvable transformation of knowledge graphs into human-oriented formatsJournal of Intelligent Information Systems10.1007/s10844-023-00809-w62:2(295-316)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10844-023-00809-w

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