A Multi-Phase Reference Matching Algorithm for Bibliometric Analysis: Design, Implementation, and Evaluation

A Multi-Phase Reference Matching Algorithm for Bibliometric Analysis: Design, Implementation, and Evaluation | 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 A Multi-Phase Reference Matching Algorithm for Bibliometric Analysis: Design, Implementation, and Evaluation Massimo Aria, Luca D'Aniello, Maria Spano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9358015/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bibliometric analyses rely on accurate citation counts, yet bibliographic databases routinely contain variant representations of the same cited reference, differing in journal abbreviation style, author name format, punctuation, or metadata completeness, that fragment citation links and distort standard indicators such as the h-index and journal impact metrics. We propose an unsupervised, multi-phase reference matching algorithm designed to consolidate these variants without requiring training data or external authority files beyond the ISO 4 List of Title Word Abbreviations (LTWA). The pipeline operates in seven phases: (i) format detection and string normalisation, which parses heterogeneous reference styles and standardises author names, titles, and pagination; (ii) ISO 4journal-name normalisation, which maps both abbreviated and full journal names to a canonical short form using the LTWA; (iii) exact matching on DOI identifiers and normalised reference strings; (iv) blocking by first-author surname, publication year, and abbreviated source title; (v) within-block fuzzy matching that combines Jaro–Winkler similarity with agglomerative hierarchical clustering to group near-duplicate references; (vi) post-processing metadata reconciliation, which merges complementary fields across matched records; and (vii) canonical representative selection, which elects the most informative variant as the group representative. Evaluation on a synthetic benchmark of 1 000 source articles, each generating up to ten controlled perturbation variants, yields precision, recall, and F1 scores above 0.95 in 15 of 17 scenarios, with the two most aggressive perturbation settings still achieving F1 ≥ 0.78. Validation on two real-world Scopus datasets demonstrates that the algorithm reduces unique cited-reference counts by 4.6–11.1 %, with corresponding increases in concentration-based bibliometric indicators. The algorithm is implemented in the open-source bibliometrix R package, which has been widely adopted in the scientometric community. reference matching bibliographic record linkage reference deduplication bibliometrics string similarity Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9358015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635407348,"identity":"dc1f84dc-b609-4b1b-94ae-6827e578b2b4","order_by":0,"name":"Massimo Aria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLCCBDDJ2MDwgYGBB8Q8QLQWxhkwLYT1QAEzD4yFT4tu+/HHHx7m2DDwzz7c+Nm27Y6MOfsBxsMf8GgxO5NjJpG4LY1B4lxis3Ru2zMey54E/A4zO5DDxpC47TADwxnGBqCWwzwGBwhpOf/88QeQFvkzjM2/LUFazj8goOVGgoEESIvBGcY2aUaQlhuEbLnxBuwXHkOgFsuecyAtDxsOnMHrsPTHH39us5GTO8P++MaPssP2BueTD3+owKMFBniQ2MBkMApGwSgYBaOAMgAA/AFWAx0RLdkAAAAASUVORK5CYII=","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":true,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Aria","suffix":""},{"id":635407349,"identity":"afad7d75-8266-40d7-b531-f0141e4e8c90","order_by":1,"name":"Luca D'Aniello","email":"","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":false,"prefix":"","firstName":"Luca","middleName":"","lastName":"D'Aniello","suffix":""},{"id":635407350,"identity":"4119898b-c5c1-4812-9833-8b2e4797123f","order_by":2,"name":"Maria Spano","email":"","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Spano","suffix":""}],"badges":[],"createdAt":"2026-04-08 14:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9358015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9358015/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108807029,"identity":"069cbfd4-03db-4ccb-b4bb-bc45aa91c21e","added_by":"auto","created_at":"2026-05-08 15:29:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":421912,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9358015/v1_covered_283275d2-49f3-45cc-894d-662674444d1e.pdf"},{"id":108752220,"identity":"766b7323-a295-4040-ad6b-169c7fbaeef8","added_by":"auto","created_at":"2026-05-08 03:59:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":187532,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9358015/v1/f23c5faa117473cda0ef8357.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Phase Reference Matching Algorithm for Bibliometric Analysis: Design, Implementation, and Evaluation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"reference matching, bibliographic record linkage, reference deduplication, bibliometrics, string similarity","lastPublishedDoi":"10.21203/rs.3.rs-9358015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9358015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Bibliometric analyses rely on accurate citation counts, yet bibliographic databases routinely contain variant representations of the same cited reference, differing in journal abbreviation style, author name format, punctuation, or metadata completeness, that fragment citation links and distort standard indicators such as the h-index and journal impact metrics. We propose an unsupervised, multi-phase reference matching algorithm designed to consolidate these variants without requiring training data or external authority files beyond the ISO 4 List of Title Word Abbreviations (LTWA).\nThe pipeline operates in seven phases: (i) format detection and string normalisation, which parses heterogeneous reference\nstyles and standardises author names, titles, and pagination; (ii) ISO 4journal-name normalisation, which maps both abbreviated and full journal names to a canonical short form using the LTWA; (iii) exact matching on DOI identifiers and normalised reference strings; (iv) blocking by first-author surname, publication year, and abbreviated source title; (v) within-block fuzzy matching that combines Jaro–Winkler similarity with agglomerative hierarchical clustering to group near-duplicate references; (vi) post-processing metadata reconciliation, which merges complementary fields across matched records; and (vii) canonical representative selection, which elects the most informative variant as the group representative.\nEvaluation on a synthetic benchmark of 1 000 source articles, each generating up to ten controlled perturbation variants, yields precision, recall, and F1 scores above 0.95 in 15 of 17 scenarios, with the two most aggressive perturbation settings still achieving F1 ≥ 0.78. 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