Author disambiguation using multi-aspect similarity indicators

Abstract

Key to accurate bibliometric analyses is the ability to correctly link individuals to their corpus of work, with an optimal balance between precision and recall. We have developed an algorithm that does this disambiguation task with a very high recall and precision. The method addresses the issues of discarded records due to null data fields and their resultant effect on recall, precision and F-measure results. We have implemented a dynamic approach to similarity calculations based on all available data fields. We have also included differences in author contribution and age difference between publications, both of which have meaningful effects on overall similarity measurements, resulting in significantly higher recall and precision of returned records. The results are presented from a test dataset of heterogeneous catalysis publications. Results demonstrate significantly high average F-measure scores and substantial improvements on previous and stand-alone techniques.

Assessing the scientific productivity of Italian forest researchers using the Web of Science, SCOPUS and SCIMAGO databases

“…Gurney et al (2011) recently reviewed multiple methods for resolving author ambiguity issues including automated computer-based algorithms, sociological and linguistic approaches. WOS and SCOPUS use different methods based on unique author identification codes for circumventing the problem.…”

“…In the case of duplication, the same author may have multiple identities in the database as a result of publishing with different affiliations or in different fields. For these cases, a diminished number of publications or citations is usually assigned to the author (Gurney et al 2011).…”

What is the Required Level of Data Cleaning? A Research Evaluation Case

“…Quite some research activities are taking place, mainly by information scientists and computer scientists in order to obtain better disambiguation tools and techniques. [1][2][3] However, the available techniques are still far from perfect (or even optimal). Therefore, it is an important question what kind of data error exists in a specific data set and whether that data error is influencing the outcomes of studies deploying those data.…”

“…That last proposition has been challenged by Fegley and Torvik [6] for very large datasets and by Kim and Diesner [5] who shows that also in smaller datasets there might be substantial error rates. The latter authors indicate, by the use of more advanced methods for disambiguation that take several types of information into account, [1] that the rate of error can be substantial, especially in areas where there are high numbers of Asian named authors.…”

“…Therefore, quite some effort is devoted to the development of better and faster tools for disambiguating of the data (e.g., Gurney et al 2012). Parallel to this, one may ask to what extent data cleaning is needed, given the intended use of the data.…”