Author Name Disambiguation in Digital Libraries
via the enrichment of hidden topics extracted from Wikipedia
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In digital libraries, ambiguous author names may occur due to the existence of identical names, name misspellings, pseudonyms. Disambiguating these author names is a major problem during data integration and document retrieval. In this study, we assume that an individual tends to create a distinctively coherent body of work that can hence form a single cluster containing all of his/her articles yet distinguishing them from those of everyone else with the same name. Still, we believe the information contained in a digital library may be not sufficient to allow an automatic detection of such clu...
In digital libraries, ambiguous author names may occur due to the existence of identical names, name misspellings, pseudonyms. Disambiguating these author names is a major problem during data integration and document retrieval. In this study, we assume that an individual tends to create a distinctively coherent body of work that can hence form a single cluster containing all of his/her articles yet distinguishing them from those of everyone else with the same name. Still, we believe the information contained in a digital library may be not sufficient to allow an automatic detection of such clusters. Hence, we exploit Topic Models, extracted from Wikipedia, to enhance records metadata and use Agglomerative Clustering to disambiguate ambiguous author names by clustering together similar records, where records in different clusters are supposed to have been written by different people.
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