Relation Classification via Convolutional Deep Neural Network
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doi.org/w38095478 →Countries where authors are citing Relation Classification via Convolutional Deep Neural Network
This map shows the geographic impact of Relation Classification via Convolutional Deep Neural Network. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Relation Classification via Convolutional Deep Neural Network with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Relation Classification via Convolutional Deep Neural Network more than expected).
Fields of papers citing Relation Classification via Convolutional Deep Neural Network
This network shows the impact of Relation Classification via Convolutional Deep Neural Network. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Relation Classification via Convolutional Deep Neural Network.
About Relation Classification via Convolutional Deep Neural Network
This paper, published in 2014, received 862 indexed citations . Written by Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou and Jun Zhao covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (810 citations), Molecular Biology (147 citations) and Information Systems (78 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w38095478.