SRREN:Self-aware and Relation-aware Recurrent Evolution Network for Temporal Knowledge Graph Reasoning

SRREN:Self-aware and Relation-aware Recurrent Evolution Network for Temporal Knowledge Graph Reasoning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SRREN:Self-aware and Relation-aware Recurrent Evolution Network for Temporal Knowledge Graph Reasoning Laibin Zhao, Kuru Ratnavelu, Ghassan Saleh ALDharhani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4880209/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Knowledge and Information Systems → Version 1 posted 16 You are reading this latest preprint version Abstract Knowledge Graph (KG) reasoning for predicting incomplete KGs has been extensively studied. However, the Temporal Knowledge Graph (TKG), which consists of KGs at various timestamps, still has significant potential for improvement in forecasting future facts. A comprehensive understanding of historical facts is crucial for accurately predicting future events. For the same timestamp, the concurrent facts have structural dependencies, and the facts have a certain order in adjacent times. At the same time, for different timestamps, the attribute features of KGs in the current timestamp have a strong correlation with the attribute features of KGs in the historical timestamp. To effectively capture and comprehend these relational features and enhance the accuracy of TKG predictions , we propose a new recursive algorithm, the Self-aware and Relation-aware Recurrent Evolution Network (SRREN). SRREN learns an evolutionary representation of time-stamped entities and relations by recursively modeling the sequence of KGs. Specifically, for evolutionary units, structural dependencies are captured through relationship awareness. At each timestamp within the TKG, the sequential character of the facts is captured by a self-aware mechanism of regression modeling and a recursive component of the gate. In addition, the addition of entity static attributes can also achieve better entity representation. Experiments on six benchmark datasets show that SRREN can improve the accuracy of predicting future facts, and its performance is better than most other current models. Temporal Knowledge Graph Evolutional Representation Learning Graph Convolution Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Knowledge and Information Systems → Version 1 posted Editorial decision: Revision requested 24 Feb, 2025 Reviews received at journal 31 Dec, 2024 Reviews received at journal 26 Dec, 2024 Reviews received at journal 22 Dec, 2024 Reviews received at journal 11 Dec, 2024 Reviewers agreed at journal 04 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviewers agreed at journal 01 Dec, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers invited by journal 28 Nov, 2024 Editor assigned by journal 14 Aug, 2024 Submission checks completed at journal 08 Aug, 2024 First submitted to journal 08 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4880209","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343707102,"identity":"6d6bc29e-237e-41ee-9f25-1755c8b7d07a","order_by":0,"name":"Laibin Zhao","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Laibin","middleName":"","lastName":"Zhao","suffix":""},{"id":343707105,"identity":"e7374bcc-b411-44d9-b557-acc2e5afc11a","order_by":1,"name":"Kuru Ratnavelu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACCSgtx8bMfPABkMHDR5SWAwwMxvzsbMkGIC1sxGpJnNnPYwbmENQiOSP34eePO+wYNxxmS6v8mmMnw8bA/PDRDTxapCXSjSUOnklmNjjMfOy27LZkoMPYjI1z8GiRk0hjkDjYxsxmALTltuQ2ZqAWHjZpAlqYfxxsq+cxOMxjViy5rZ6wFmmJNDagLYclJJt5zBg/bjtMWItkzzM2i7Ntxw34mdmSpRm3HedhYybgF4njacw3Ktuq69v4Dx/8+HNbtT0/e/PDx/i0oABmHjBJrHIQYPxBiupRMApGwSgYMQAAZoBBsT3dh/AAAAAASUVORK5CYII=","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"","firstName":"Kuru","middleName":"","lastName":"Ratnavelu","suffix":""},{"id":343707106,"identity":"ff820092-66f1-4ce2-8c4f-e3ebfb33e4dc","order_by":2,"name":"Ghassan Saleh ALDharhani","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Ghassan","middleName":"Saleh","lastName":"ALDharhani","suffix":""}],"badges":[],"createdAt":"2024-08-08 10:17:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4880209/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4880209/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10115-026-02713-5","type":"published","date":"2026-03-13T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104739627,"identity":"8622f96b-599e-4537-81fa-3787f4f0e7f9","added_by":"auto","created_at":"2026-03-16 16:10:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2059649,"visible":true,"origin":"","legend":"","description":"","filename":"SRRENZhaoLaibin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4880209/v1_covered_8c354b37-6861-4dc3-b675-986af107f6c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SRREN:Self-aware and Relation-aware Recurrent Evolution Network for Temporal Knowledge Graph Reasoning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Temporal Knowledge Graph, Evolutional Representation Learning, Graph Convolution Network","lastPublishedDoi":"10.21203/rs.3.rs-4880209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4880209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Knowledge Graph (KG) reasoning for predicting incomplete KGs has been extensively studied. 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