MORE: Toward Improving Author Name Disambiguation in Academic Knowledge Graphs

Gong, Jianya; Fang, Xiaohan; Peng, Jiquan; Zhao, Yi; Zhao, Jisheng; Wang, Chenlong; Li, Yangyang; Zhang, Jingyi; Drew, Steve

doi:10.1007/s13042-022-01686-5

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…In order to overcome the problem, some recent research [10] has focused on using side information, such as user attribution and item description. The representation of relationships between users and items can be enhanced and complemented by the side information of the knowledge graph (KG) [11,12]. From certain perspectives, KGs can partially mitigate the issue of the data sparsity problem.…”

Section: Introductionmentioning

confidence: 99%

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Peng,

Gong,

Zhou

et al. 2024

Electronics

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Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.

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Section: Introductionmentioning

confidence: 99%

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Peng,

Gong,

Zhou

et al. 2024

Electronics

Self Cite

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PubMed Computed Authors in 2024: an open resource of disambiguated author names in biomedical literature

Tian,

Chen,

Comeau

et al. 2024

Bioinformatics

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Summary Over 55% of author names in PubMed are ambiguous: the same name is shared by different individual researchers. This poses significant challenges on precise literature retrieval for author name queries, a common behavior in biomedical literature search. In response, we present a comprehensive dataset of disambiguated authors. Specifically, we complement the automatic PubMed Computed Authors algorithm with the latest ORCID data for improved accuracy. As a result, the enhanced algorithm achieves high performance in author name disambiguation, and subsequently our dataset contains more than 21 million disambiguated authors for over 35 million PubMed articles and is incrementally updated on a weekly basis. More importantly, we make the dataset publicly available for the community such that it can be utilized in a wide variety of potential applications beyond assisting PubMed's author name queries. Finally, we propose a set of guidelines for best practices of authors pertaining to use of their names. Availability and Implementation The PubMed Computed Authors dataset is publicly available for bulk download at: https://ftp.ncbi.nlm.nih.gov/pub/lu/ComputedAuthors/. Additionally, it is available for query through web API at: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/authors/ Supplementary information Supplementary data are available at Bioinformatics online.

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