Research on the Construction and Application of Problem-Method-Oriented Academic Graph Empowered by LLM

Research on the Construction and Application of Problem-Method-Oriented Academic Graph Empowered by LLM | 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 Research on the Construction and Application of Problem-Method-Oriented Academic Graph Empowered by LLM Qigang Liu, Yinfan Wang, Lifeng Mu, Jun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5624904/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ literature review. In this circumstance, developing useful tool for achieving efficient literature management is of high value. Traditional literature management tools, such as tools for key word searching, paper recommendation, relation visualization, and keyword cloud drawing, are not suitable for conducting content-level literature review. To address the issues of traditional literature management tools, a novel problem and method-oriented fine-grained academic graph is proposed to facilitate the exploration of research questions, methodologies, study perspectives, and their connections hidden in massive literature. For building such graph, a new ontology dedicated for describing the features of research paper is developed, an innovative multi-relation join extraction model is proposed, and a creative approach for leveraging the Large Language Models (LLM) to augment the triplet extraction results generated by supervised-learning model is developed. Experiments on widely used benchmark datasets show that the proposed multi-relation extraction model is able to achieve at least 8.01% and 8.65% improvement on entity identification and relation classification respectively, compared with state-of-the-art models. The visualized demonstration of the proposed graph shows that our graph is capable of accurately capturing the problem network, method network and hot topics hidden in massive literature. The Q&A system supported by the proposed graph demonstrates that our graph is really helpful for conducting literature review. The data and code of this work are available at https://github.com/asilcr/AcademicGraph . Named Entity Recognition Relation Extraction Large Language Model Academic Graph Artificial Intelligence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 May, 2025 Reviews received at journal 14 May, 2025 Editor assigned by journal 25 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 28 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5624904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443096272,"identity":"3f0bba4d-5047-4ebe-8f35-a9be37c57ece","order_by":0,"name":"Qigang Liu","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Qigang","middleName":"","lastName":"Liu","suffix":""},{"id":443096273,"identity":"fcb86256-56b8-4bd8-997f-51ec574227cb","order_by":1,"name":"Yinfan Wang","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Yinfan","middleName":"","lastName":"Wang","suffix":""},{"id":443096274,"identity":"69445821-b94e-4171-b4e9-7f598b07c361","order_by":2,"name":"Lifeng Mu","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Mu","suffix":""},{"id":443096275,"identity":"c84c8664-1ea6-4ef5-9d54-4527eb9965b9","order_by":3,"name":"Jun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBAC9gYGBmYGAxsIj4cYLYwQLWkQ1SRoYThMipYZuQeYGQrOJ+7nP8D44G0bg7zBAYJa8hKADrud2CORwGw4t43BcANhLTkGUC0MbNK8bQyMxGo5l9jDf4D9N1CLPUEtghAtBxJ7GBLYmIFaEglqkeZ5Y3CYwSDZuOdGYrPknHMSyTMJaeFjzzF8/OOPnWx7/+GDH96U2dj2EdICAlA1oChikCBC/SgYBaNgFIwCggAA/p45sNnL2GcAAAAASUVORK5CYII=","orcid":"","institution":"FEIT, University of Technology Sydney","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-11 13:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5624904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5624904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80706152,"identity":"0489be70-1116-4fa6-a83f-091db7006152","added_by":"auto","created_at":"2025-04-16 08:30:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2818139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript202544.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5624904/v1_covered_24658ac4-d365-4cee-806a-e27f5becdae4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Construction and Application of Problem-Method-Oriented Academic Graph Empowered by LLM","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Named Entity Recognition, Relation Extraction, Large Language Model, Academic Graph, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-5624904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5624904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers\u0026rsquo; literature review. In this circumstance, developing useful tool for achieving efficient literature management is of high value. Traditional literature management tools, such as tools for key word searching, paper recommendation, relation visualization, and keyword cloud drawing, are not suitable for conducting content-level literature review. To address the issues of traditional literature management tools, a novel problem and method-oriented fine-grained academic graph is proposed to facilitate the exploration of research questions, methodologies, study perspectives, and their connections hidden in massive literature. For building such graph, a new ontology dedicated for describing the features of research paper is developed, an innovative multi-relation join extraction model is proposed, and a creative approach for leveraging the Large Language Models (LLM) to augment the triplet extraction results generated by supervised-learning model is developed. Experiments on widely used benchmark datasets show that the proposed multi-relation extraction model is able to achieve at least 8.01% and 8.65% improvement on entity identification and relation classification respectively, compared with state-of-the-art models. The visualized demonstration of the proposed graph shows that our graph is capable of accurately capturing the problem network, method network and hot topics hidden in massive literature. The Q\u0026amp;A system supported by the proposed graph demonstrates that our graph is really helpful for conducting literature review. 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