ChronoBridge: A Novel Framework for Enhanced Temporal and Relational Reasoning in Temporal Knowledge Graphs

ChronoBridge: A Novel Framework for Enhanced Temporal and Relational Reasoning in Temporal Knowledge Graphs | 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 ChronoBridge: A Novel Framework for Enhanced Temporal and Relational Reasoning in Temporal Knowledge Graphs Qian Liu, Siling Feng, MengXing Huang, Uzair Aslam Bhatti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4684006/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Artificial Intelligence Review → Version 1 posted 12 You are reading this latest preprint version Abstract The prediction task of entities and relationships in Temporal Knowledge Graph (TKG) extrapolation is crucial and extensively studied. Mainstream algorithms like Gated Recurrent Unit (GRU) models primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entity and relationship features during decoding. This bias ultimately leads to loss of detail and inadequate prediction accuracy during the inference process .Addressing this issue, a novel ChronoBridge framework is proposed, featuring a dual mechanism of a Chronological node Encoder and a Bridged Feature Fusion Decoder. Specifically, the Temporal Node Encoder employs an advanced recursive neural network with enhanced GRU in an autoregressive manner to model historical KG sequences, thereby accurately capturing entity changes over time and significantly enhancing the model’s ability to identify and encode temporal patterns of facts across the timeline. Meanwhile, the Bridge Feature Fusion Decoder utilizes a new variant of GRU and a multi-layer perceptron mechanism during the prediction phase to extract entity and relationship features and fuse them for inference, thereby strengthening the model’s reasoning capabilities for future events.Test results on three standard datasets demonstrate significant performance improvements compared to existing techniques, with a 25.21% increase in Mean Reciprocal Rank (MRR) accuracy for prediction tasks and a 39.38% improvement in relationship inference, thus validating its effectiveness. This breakthrough not only enhances understanding of temporal evolution in knowledge graphs but also paves the way for future research and applications in TKG reasoning. Temporal Knowledge Graph reasoning Gated Recurrent Unit Chronological node Encoder Bridged Feature Fusion Decoder Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Artificial Intelligence Review → Version 1 posted Editorial decision: Revision requested 15 Aug, 2024 Reviews received at journal 20 Jul, 2024 Reviews received at journal 14 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviewers agreed at journal 06 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 04 Jul, 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-4684006","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329544584,"identity":"eec2f603-3d84-4dcc-bf4c-169d62330eb2","order_by":0,"name":"Qian Liu","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Liu","suffix":""},{"id":329544585,"identity":"6a75029e-946a-4bd7-b889-f93741abcc97","order_by":1,"name":"Siling Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBAC9gYQaWDBw8DAfAzIgArz4NHCcwCsRQKohi2NFC0MEiCmGZIwPi3svYdf/iiQkDHnX/PtwY+CO/LmEgmMD962Mcib49LCcy7NmgfoMMsZb7cb9hg8M9w5I4HZcG4bg+HOBuxa7CVyzIxBfjG4cXYbkDzMuOFGAps0bxtDgsEBHLbIvzEz/AHWcuaZ5B+Dw/ZALey/8WqR4DF+AHKYwfkeNmmgLYkgW5jxauHJMWMGa7nBZiYtY3A4ecOZh82Sc85JGG7ApYX9jPHHH39s7A3OH34m+ebPYdsNx5MPfnhTZiOPyxYgYJMAUxIJMAHGBgZITOEEzB/AFD9uQ0fBKBgFo2CEAwCgeVexkSNbqgAAAABJRU5ErkJggg==","orcid":"","institution":"Hainan University","correspondingAuthor":true,"prefix":"","firstName":"Siling","middleName":"","lastName":"Feng","suffix":""},{"id":329544586,"identity":"481db19f-a51e-463f-b3df-64e4c5208ee9","order_by":2,"name":"MengXing Huang","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"MengXing","middleName":"","lastName":"Huang","suffix":""},{"id":329544587,"identity":"a316d0b9-bc71-4d29-9720-f93ec83ee8b8","order_by":3,"name":"Uzair Aslam Bhatti","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Uzair","middleName":"Aslam","lastName":"Bhatti","suffix":""}],"badges":[],"createdAt":"2024-07-04 05:53:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4684006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4684006/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10462-024-10983-0","type":"published","date":"2024-10-22T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67682737,"identity":"c7800f40-232a-4865-a2d6-60013c3206c6","added_by":"auto","created_at":"2024-10-28 16:14:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3847229,"visible":true,"origin":"","legend":"","description":"","filename":"ChronoBridgeAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4684006/v1_covered_bf9aea2c-04b4-444a-8a90-4292e88ae27f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ChronoBridge: A Novel Framework for Enhanced Temporal and Relational Reasoning in Temporal Knowledge Graphs","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":"artificial-intelligence-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aire","sideBox":"Learn more about [Artificial Intelligence Review](http://link.springer.com/journal/10462)","snPcode":"10462","submissionUrl":"https://submission.nature.com/new-submission/10462/3","title":"Artificial Intelligence Review","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Temporal Knowledge Graph reasoning, Gated Recurrent Unit, Chronological node Encoder, Bridged Feature Fusion Decoder","lastPublishedDoi":"10.21203/rs.3.rs-4684006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4684006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The prediction task of entities and relationships in Temporal Knowledge Graph (TKG) extrapolation is crucial and extensively studied. 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