Automatic Navbox Generation by Interpretable Clustering over Linked Entities

Xie, Chenhao; Chen, Lihan; Liang, Jiaqing; Zhang, Kezun; Tong, Hanghang; Wang, Haixun; Wang, Wei

doi:10.1145/3132847.3132899

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…For very large classification space, extreme multi-label classification is proposed, e.g., a method based on graph embedding (Tagami, 2017 ), a method based on convolutional neural network (CNN) (Liu et al, 2017 ), and a method based on attention model of neural networks (Wang et al, 2018 ). Moreover, label hierarchy also can be considered so that part-of, is-a, and inclusion relationships are extracted from external data sources such as Wikipedia in the classification task (Bairi et al, 2016 ; Xie et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence

Kurakawa

Sun

Ando³

2020

Front. Big Data

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A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.

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