Chapter

Knowledge Discovery in Databases: PKDD 2006

Volume 4213 of the series Lecture Notes in Computer Science pp 536-544

Efficient Name Disambiguation for Large-Scale Databases

  • Jian HuangAffiliated withCarnegie Mellon UniversityCollege of Information Sciences and Technology, The Pennsylvania State University
  • , Seyda ErtekinAffiliated withCarnegie Mellon UniversityDepartment of Computer Science and Engineering, The Pennsylvania State University
  • , C. Lee GilesAffiliated withCarnegie Mellon UniversityCollege of Information Sciences and Technology, The Pennsylvania State UniversityDepartment of Computer Science and Engineering, The Pennsylvania State University

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Abstract

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.