A hybrid knowledge-based framework for author name disambiguation

Protasiewicz, Jarosław; Dadas, Sławomir

doi:10.1109/smc.2016.7844305

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…The labeling algorithm [9] combines mutual information (MI) with tf to score the weights. For each feature word x i in cluster C k , the score is calculated by formula (6). MIðx i , nameÞ measures the mutual information between the feature word and personal name.…”

Section: Framework and Methodologymentioning

confidence: 99%

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Chinese Personal Name Disambiguation Based on Clustering

Fan

Liu

2021

Wireless Communications and Mobile Computing

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Personal name disambiguation is a significant issue in natural language processing, which is the basis for many tasks in automatic information processing. This research explores the Chinese personal name disambiguation based on clustering technique. Preprocessing is applied to transform raw corpus into standardized format at the beginning. And then, Chinese word segmentation, part-of-speech tagging, and named entity recognition are accomplished by lexical analysis. Furthermore, we make an effort to extract features that can better disambiguate Chinese personal names. Some rules for identifying target personal names are created to improve the experimental effect. Additionally, many calculation methods of feature weights are implemented such as bool weight, absolute frequency weight, tf-idf weight, and entropy weight. As for clustering algorithm, an agglomerative hierarchical clustering is selected by comparison with other clustering methods. Finally, a labeling approach is employed to bring forward feature words that can represent each cluster. The experiment achieves a good result for five groups of Chinese personal names.

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Section: Framework and Methodologymentioning

confidence: 99%

“…Moreover, negative samples were employed to train a global model. Protasiewicz and Dadas [6] produced a hybrid framework considering both rule-based method and agglomerative hierarchical clustering. Rules were generated from the knowledge of experts, analysis, and so forth.…”

Section: Related Workmentioning

confidence: 99%

Chinese Personal Name Disambiguation Based on Clustering

Fan

Liu

2021

Wireless Communications and Mobile Computing

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“…In our implementation, two authors are compared with each other if their names have a Jaro-Winkler similarity of at least 0.9 following Donner (2014) and Hajra, Radevski, and Tochtermann (2015). Concerning the rules for the author name disambiguation, we use factors that have already proven to be reliable in the literature and which are rated according to their importance (Caron & van Eck, 2014;Cen, Dragut, Si, & Ouzzani, 2013;Dendek, Bolikowski, & Lukasik, 2012;Protasiewicz & Dadas, 2016).…”

Section: Author Name Disambiguationmentioning

confidence: 99%

“…On the one hand, we can use explicit information that comes directly from the data set metadata. On the other hand, we can use implicit evidence derived from the data set metadata (Ferreira, Gonçalves, & Laender, 2012;Protasiewicz & Dadas, 2016).…”

Section: Author Name Disambiguationmentioning

confidence: 99%

The data set knowledge graph: Creating a linked open data source for data sets

Färber

Lamprecht

2021

Quantitative Science Studies

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Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.

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“…In [5], a scheme was proposed to discern individuals with the same name using the metadata of academic search sites and determining name matches based on the similarity of attributes between two different papers. Furthermore, studies have been conducted to establish rules for calculating the similarity of attributes between two different papers and conduct cluster analysis based on the calculated similarity [6,7]. A scheme that uses the metadata of a paper as a feature of deep neural networks has been proposed to discern individuals with the same name [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Name Disambiguation Scheme Based on Heterogeneous Academic Sites

Choi,

Jang,

Song

et al. 2023

Applied Sciences

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Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher’s work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme for researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, we implement the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multi-classifier approach. We consider various researchers’ achievements and metadata of articles registered in various academic search sites. The proposed scheme showed an exceptionally high F1-measure value of 0.99. In this paper, we propose a multi-classifier that executes the most appropriate disambiguation scheme depending on the inputted metadata. The proposed multi-classifier shows the high F1-measure value of 0.67.

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A hybrid knowledge-based framework for author name disambiguation

Cited by 9 publications

References 21 publications

Chinese Personal Name Disambiguation Based on Clustering

Chinese Personal Name Disambiguation Based on Clustering

The data set knowledge graph: Creating a linked open data source for data sets

Name Disambiguation Scheme Based on Heterogeneous Academic Sites

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