Disambiguating authors in academic publications using random forests

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...

• Type: Conference
• Year: 2009
• Where: JCDL
• Authors: Pucktada Treeratpituk, C. Lee Giles
• Keywords: Author Disambiguation, Disambiguation, Education, JCDL 2009, Random Forests