Evaluación del algoritmo de desambiguación de autores de AMiner en un metabuscador académico de Ciencias de la Computación

Author disambiguation is a problem of considerable relevance for academic information retrieval systems. A name disambiguation algorithm implemented in AMiner represents one of the approaches based on Machine Learning with the greatest impact in the present. This work presents an evaluation of the name disambiguation algorithm of AMiner for author disambiguation in the context of an academic metasearcher of Computer Science. Experimental results with data generated by the academic metasearcher indicate an average performance similar to the reference. Likewise, experimental results led to identify special cases of author names that present low performance compared with the average. This finding allowed the identification of an apparent association between the low performance of the algorithm in the context of several authors with the same name and with a low number of publications.La desambiguación de autores es un problema de gran relevancia para los sistemas de recuperación de información del ámbito académico. El algoritmo de desambiguación de nombres de AMiner representa uno de los enfoques basados en Aprendizaje Automático con mayor impacto en la actualidad. En este trabajo, se presenta una evaluación del algoritmo de desambiguación de nombres de AMiner para la desambiguación de autores en el contexto de un metabuscador académico del área de las Ciencias de la Computación. Los resultados experimentales con datos generados por el metabuscador académico dan cuenta de un desempeño promedio similar a la referencia. Asimismo, las evaluaciones experimentales permitieron identificar casos especiales de nombres de autores en el que el algoritmo presenta un bajo desempeño en comparación con el promedio. Este hallazgo permitió identificar una asociación aparente entre el bajo desempeño del algoritmo en contextos de varios autores con un mismo nombre y con pocas publicaciones

How reliable are unsupervised author disambiguation algorithms in the assessment of research organization performance?

The paper examines extent of bias in the performance rankings of research organisations when the assessments are based on unsupervised author-name disambiguation algorithms. It compares the outcomes of a research performance evaluation exercise of Italian universities using the unsupervised approach by Caron and van Eck (2014) for derivation of the universities' research staff, with those of a benchmark using the supervised algorithm of D'Angelo, Giuffrida, and Abramo (2011), which avails of input data. The methodology developed could be replicated for comparative analyses in other frameworks of national or international interest, meaning that practitioners would have a precise measure of the extent of distortions inherent in any evaluation exercises using unsupervised algorithms. This could in turn be useful in informing policy-makers' decisions on whether to invest in building national research staff databases, instead of settling for the unsupervised approaches with their measurement biases

Effect of forename string on author name disambiguation

In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performance of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled data sets under varying ratios and lengths of full forenames, reflecting real‐world scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). The results show that increasing the ratios of full forenames substantially improves both heuristic and machine‐learning‐based disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonyms are prevalent. As the ratios of full forenames increase, however, they become marginal compared to those by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation methods compared to using full‐length strings. These findings provide practical suggestions, such as restoring initialized forenames into a full‐string format via record linkage for improved disambiguation performances.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/1/asi24298.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/2/asi24298_am.pd