Design of Knowledge Service Model Combining Dynamic Knowledge Graph and Enterprise Risk Management based on Bidirectional Encoder Representation from Transformers Bidirectional Long Short- Term Memory

Design of Knowledge Service Model Combining Dynamic Knowledge Graph and Enterprise Risk Management based on Bidirectional Encoder Representation from Transformers Bidirectional Long Short- Term Memory | 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 Design of Knowledge Service Model Combining Dynamic Knowledge Graph and Enterprise Risk Management based on Bidirectional Encoder Representation from Transformers Bidirectional Long Short- Term Memory Yu Jia-yin, Jiang Jiang, Ya-dong Shi, Zi-zhen LI, Shen Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6692424/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Discover Computing → Version 1 posted 15 You are reading this latest preprint version Abstract A dynamic knowledge graph and knowledge service model were constructed to address the risk management needs of enterprises. By extracting, integrating, and processing knowledge entities, a complete knowledge graph is formed, and then a knowledge service model is designed to achieve intelligent and dynamic risk management. The experiment shows that the entity extraction method based on TextRank algorithm proposed by the research has an accuracy of 82.7%, a recall rate of 80.9%, and an F1 score of 81.8% in Class A datasets. The relationship fusion method of the Bidirectional Encoder Representation from Transformers Bi directional Long Short-Term Memory (BERT-Bi-LSTM) model based on transformers proposed by the research has an accuracy of about 87.8% for knowledge graph relationship fusion. The response time of the constructed enterprise risk management knowledge service model to 1000 risk management transaction requests is about 11.3 minutes, the maximum sustainable throughput is 1918TPS, the CPU utilization rate is 54.7%, and the memory usage is 3.0GB. The above results indicate that the dynamic knowledge graph and the knowledge service model of enterprise risk management perform well in multiple core indicators, which can effectively improve the intelligence and dynamic level of enterprise risk management. Dynamic knowledge graph Enterprise risk management Knowledge service model Knowledge entity extraction Integration of knowledge relationships Bidirectional Encoder Representation from Transformers Bi directional Long Short-Term Memory(BERT-Bi-LSTM) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 31 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor invited by journal 09 Jun, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 19 May, 2025 First submitted to journal 18 May, 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. 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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-6692424","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472021216,"identity":"a0d270ef-329b-4c96-85e7-592e6ef47d4e","order_by":0,"name":"Yu 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The above results indicate that the dynamic knowledge graph and the knowledge service model of enterprise risk management perform well in multiple core indicators, which can effectively improve the intelligence and dynamic level of enterprise risk management.\u003c/p\u003e","manuscriptTitle":"Design of Knowledge Service Model Combining Dynamic Knowledge Graph and Enterprise Risk Management based on Bidirectional Encoder Representation from Transformers Bidirectional Long Short- Term Memory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 03:57:20","doi":"10.21203/rs.3.rs-6692424/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-31T17:10:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-13T02:13:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T17:13:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270304495018742619842477273299827081841","date":"2025-07-07T03:45:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113646013250363288392443501684311086014","date":"2025-07-06T00:48:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T02:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9236963922880039156844425472886261431","date":"2025-07-04T00:58:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T03:09:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125409830831155372493305951315140544900","date":"2025-06-16T13:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300511054739301193814958299191206551988","date":"2025-06-16T02:05:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T09:58:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T12:29:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T12:46:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T12:42:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2025-05-18T14:42:57+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"f103578e-ab72-4729-b714-698bfa633ac5","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:10:55+00:00","versionOfRecord":{"articleIdentity":"rs-6692424","link":"https://doi.org/10.1007/s10791-026-10086-0","journal":{"identity":"discover-computing","isVorOnly":false,"title":"Discover Computing"},"publishedOn":"2026-04-09 15:57:26","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2025-06-17 03:57:20","video":"","vorDoi":"10.1007/s10791-026-10086-0","vorDoiUrl":"https://doi.org/10.1007/s10791-026-10086-0","workflowStages":[]},"version":"v1","identity":"rs-6692424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6692424","identity":"rs-6692424","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}