How can author‑name ambiguity in bibliographic databases be resolved to confirm whether two similar names refer to the same researcher?
Executive summary
Resolving whether two similar author names represent the same researcher requires combining metadata signals, algorithmic clustering, external identifiers and—where ambiguity persists—human verification; no single technique is sufficient at scale [1] [2] [3]. Contemporary best practice couples structural signals (co‑authorship, citations, venues, years) with semantic or identifier evidence (affiliations, emails, ORCID/external databases) and targeted manual checks of hard cases [4] [5] [6].
1. Use rich bibliographic features, not names alone
Historical and survey literature shows that relying on surname + initial is a blunt tool that produces both lumping and splitting errors; advanced approaches therefore compare coauthors, titles, venues, subject headings and publication years to compute similarity between records [7] [2] [4]. Empirical MEDLINE work demonstrated that augmenting name matching with bibliographic metadata yields high recall and low lumping/splitting rates when applied at scale [1].
2. Graph and network signals are uniquely powerful
Citation and co‑authorship networks provide structural fingerprints: papers written by the same person tend to cluster via shared coauthors, references and citation patterns, enabling algorithms to separate homonyms even with sparse name strings [4] [8]. Large-scale studies applying bibliographic coupling and network similarity have shown strong discriminative power and form the backbone of many successful disambiguation pipelines [9] [4].
3. Machine learning—supervised, unsupervised and hybrid—scales the work
A long line of work surveys unsupervised clustering, heuristic hierarchical methods and supervised classifiers that learn which features best signify identity; recent deep and hybrid models further improve precision by focusing ML effort only on ambiguous clusters [3] [10] [11]. The newest hybrid pipelines combine fast structural disambiguation with targeted language models or classifiers to resolve the remaining hard cases, improving F1 and precision on benchmark sets [9] [11].
4. Aggregate external databases and persistent identifiers when possible
Linking internal bibliographic records to external sources—ORCID, institutional profiles, ResearcherID, homepages or aggregated databases—reduces ambiguity caused by initials or name variants; PubMed‑focused work finds that aggregating multiple bibliographic sources materially improves disambiguation where internal metadata is incomplete [5] [12] [13]. However, external profiles themselves can contain errors or omissions, so they are a strong but not infallible signal [13].
5. Minimal‑evidence heuristics help low‑metadata contexts
Where available metadata is sparse, pragmatic heuristics using minimal evidence such as affiliation, publication year proximity and limited coauthor overlap can still separate many identities; dedicated heuristic systems have been proposed to operate under such constraints and reduce false merges [6]. These approaches are valuable for older records or small publishers that lack robust metadata [11] [6].
6. Evaluate, calibrate and surface uncertainty
Evaluation against labeled gold standards and multiple datasets is essential because different methods err in different ways; comparative studies recommend triangulating results across labeled corpora, block sizes and error metrics to understand lumping vs splitting tradeoffs [8] [2]. Practical systems should surface confidence scores and route low‑confidence name pairs for manual curation rather than making blind merges [1] [9].
7. Human review remains the final arbiter for difficult cases
Even the best automated pipelines leave a tail of ambiguous clusters—cases with common names, multiple affiliations, or name variants—that require manual inspection or author confirmation; published MEDLINE and PubMed projects created gold‑standard clusters by combining algorithmic output with homepage and researcher‑profile reconciliation [1] [13]. The pragmatic architecture is therefore automated bulk disambiguation plus selective human verification.
Conclusion: a practical recipe
Combine metadata similarity (coauthors, venues, years), network structure (co‑authorship/citation clustering), external identifiers (ORCID, institutional pages), and layered ML that focuses expensive semantic models on ambiguous clusters; evaluate against labeled datasets and escalate persistent low‑confidence pairs for human verification—this multi‑signal, triage‑driven approach is the field consensus for confirming whether two similar names are the same researcher [4] [9] [5].