Method and device for author naming disambiguation and electronic equipment

Abstract

The invention discloses a method and a device for author naming disambiguation and electronic equipment. The method comprises the steps of determining a unique author of a paper from an academic dataset by utilizing a pre-trained classification model according to related information of the paper; searching the academic data set to obtain an alternative paper set by utilizing related information of papers for papers for which a unique author cannot be determined; and clustering the papers in the alternative paper set to obtain a plurality of categories, performing reverse classification on thepapers in the alternative paper set to determine the categories where the papers are located, and creating unique authors for the papers according to the categories. In actual work, the method provided by the invention is adopted to perform naming disambiguation on a big data set, and an efficient and extensible effect is achieved on the premise of not losing recall and precision. Therefore, themethod provided by the invention provides an effective solution for naming disambiguation of an oversized data set.

Description

technical field [0001] The invention relates to the technical field of electronic data processing, in particular to a method, device and electronic equipment for author name disambiguation. Background technique [0002] Author name ambiguity is a frequently encountered problem in academic datasets such as digital libraries. The main reason for this problem is that different authors may publish papers with the same name, and the same author may publish papers with different names due to reasons such as abbreviations and nicknames. Name disambiguation is the key to solve this problem. Name disambiguation has been regarded as a challenging problem in many applications such as document management in digital libraries, scholarly search, and social network analysis. [0003] Currently, name disambiguation is usually performed independently using clustering or classification algorithms. For example, the Gradient Boosted Trees classification method proposed by Kunho Kim achieved ...

AI Technical Summary

Problems solved by technology

[0004] However, for a large data set including hundreds of millions of data such as AMiner (the AMiner data set itself has 130 million experts and 200 million papers, compared with DBLP and PubMed, the data volume is dozens of times), The author's naming disambiguation is a more complex work, which must consider both the accuracy of the algorithm and the speed of the algorithm; both the existing data and the daily growth of new data

The above existing algorithms are only suitable for operating on relatively small data sets. Once they are run on large data sets, there will be recall problems, and the speed is slow and the precision is insufficient.

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