Web-Scale Academic Name Disambiguation: The WhoIsWho Benchmark, Leaderboard, and Toolkit

Chen, Bo; Zhang, Jing; Zhang, Fanjin; Han, Tianyi David; Cheng, Yuqing; Li, Xiaoyan; Dong, Yuxiao; Tang, Jie

doi:10.1145/3580305.3599930

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…AMiner-WhoIsWho ( Chen et al (2023a) ) is the world’s largest manually-labeled name disambiguation benchmark. Authors have released 3 versions of the dataset containing more than 1,000,000 articles;…”

Section: Datasets and Benchmarksmentioning

confidence: 99%

Graph-based methods for Author Name Disambiguation: a survey

De Bonis,

Falchi,

Manghi

2023

PeerJ Computer Science

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Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers’ activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.

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Section: Datasets and Benchmarksmentioning

confidence: 99%

Graph-based methods for Author Name Disambiguation: a survey

De Bonis,

Falchi,

Manghi

2023

PeerJ Computer Science

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High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graph

Bai,

Bu,

2024

Concurrency and Computation

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SummaryIn person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets.

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