Distinct Author Identification System for Bibliographic Disambiguation
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Solution Overview
Problem
Existing systems for bibliographic citation and authorship attribution in scientific research face challenges due to author name ambiguity, incomplete information, and common names, leading to inaccurate identification and attribution of authorship, which affects the integrity and reliability of databases and research outcomes.
Innovation Solution
The Distinct Author Identification System (DAIS) employs advanced techniques to disambiguate author information by using reliable data elements such as email addresses and co-citation, forming author entity clusters, and assigning unique codes to establish an authority database, thereby improving the accuracy of authorship linking and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If author names are used in abbreviated form or common names for citation, then ease of operation is improved, but measurement precision of author identification deteriorates
Solution Approach 1:
The patent introduces an authority database as an intermediary between citation and author identification. This database stores disambiguated author information with unique identifiers, acting as a mediator that resolves the conflict between using simple author names for citation and needing precise author identification. The system matches cited author names against the authority database to accurately identify authors while maintaining ease of citation.
2Ease of operation
If traditional bibliographic citation methods are used, then ease of operation is improved, but reliability of authorship attribution deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously refines author identification by comparing cited authors against the authority database. The system provides feedback on identification confidence levels and allows for correction and verification of author attributions. This feedback loop improves reliability while maintaining ease of operation through automated processes.
3Measurement precision
If advanced disambiguation techniques are implemented, then measurement precision of author identification is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and disambiguating author information before it enters the citation system. The authority database is pre-populated with verified author identities and unique identifiers. This preliminary disambiguation reduces the complexity of real-time resolution, as the system only needs to perform matching operations rather than complex disambiguation during citation operations.
4Reliability
If authority database with unique codes is created, then reliability of database integration is improved, but device complexity increases
Solution Approach 1:
The authority database serves multiple functions: it stores author information, provides unique identifiers for disambiguation, enables matching and verification, and supports various citation formats. This multi-functionality justifies the added complexity by providing a centralized, reliable foundation that improves database integration across the entire system rather than requiring separate solutions for each function.
Data Source
AI summary
The present invention provides a Distinct Author Identification System (“DAIS”) for disambiguating data to discern author entities and link or associate authorships with such author entities. The invention provides powerful disambiguation processes applied across one or more databases to yield a disambiguated authority database of authors. An entire database of publications may be processed by the DAIS to group/link authorships and to identify author entities. The author entities may then be matched or associated with actual authors to establish an authority database of authors. After initial evaluation, the DAIS may be used to reevaluate some or all of the database(s) and/or the authority database established by the DAIS may be used to add or update information. DAIS may use “hierarchical clustering” to link authorships and identify authors based on authorship similarity. DAIS evaluates the likelihood that authorships are from the same author.