NDFMF: An Author Name Disambiguation Algorithm Based on the Fusion of Multiple Features
| Autor: | Yongping Li, Mark Liptrott, Xiaolong Xu, Nik Bessis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Fusion
Similarity (geometry) Computer science media_common.quotation_subject 05 social sciences Feature extraction Cognitive neuroscience of visual object recognition 050905 science studies Feature (machine learning) Quality (business) 0509 other social sciences 050904 information & library sciences Author name Algorithm media_common |
| Zdroj: | COMPSAC (2) |
| DOI: | 10.1109/compsac.2018.10226 |
| Popis: | Author name disambiguation is a very important and complex research topic. During the retrieval and research of literature the quality of the investigation results has been reduced because of the high probability of different authors sharing the same name, which lengthens the whole cycle of the scientific research. Therefore, it is necessary to find a reasonable and efficient method to distinguish the different authors who share the same name. In this paper, an author name disambiguation algorithm based on the fusion of multiple features (NDFMF) is proposed. First we proposed a single feature similarity detection algorithm (SFSD). SFSD is used to compute the degree of similarity between two features of a paper and to assess the threshold value. Then, SFSD is used to realize the preliminary SFSD-based disambiguation algorithm (SFSDD). Furthermore, different features are evaluated according to the disambiguation results of author names and the evaluation metrics, including precision, recall and F-measure with SFSDD. The evaluation parameter of weight (W) is introduced to express each feature's influence in disambiguation. NDFMF can disambiguate author names more efficiently based on the fusion of multiple features. Experiments were implemented to test the performance of NDFMF. Experimental results show that NDFMF was effective in the disambiguation precision, recall and F-measure. |
| Databáze: | OpenAIRE |
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