Disambiguating authors in academic publications using random forests

Pucktada Treeratpituk, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

127 Scopus citations


Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pairwise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.

Original languageEnglish (US)
Title of host publicationJCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
Number of pages10
StatePublished - 2009
Event2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States
Duration: Jun 15 2009Jun 19 2009

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996


Other2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
Country/TerritoryUnited States
CityAustin, TX

All Science Journal Classification (ASJC) codes

  • General Engineering


Dive into the research topics of 'Disambiguating authors in academic publications using random forests'. Together they form a unique fingerprint.

Cite this