Mining and searching association relation of scientific papers based on deep learning
Jie Song, Meiyu Liang, Zhe Xue, Feifei Kou, Ang Li
TL;DR
This paper explores how deep learning can be used to analyze and search for associations among scientific papers, revealing complex data relationships to aid researchers.
Contribution
It introduces a novel deep learning approach for mining and searching association relationships in scientific paper data.
Findings
Effective identification of paper correlations
Enhanced search capabilities for scientific data
Insights into data characteristics and laws
Abstract
There is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applications to serve scientific researchers. Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.
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Taxonomy
TopicsE-commerce and Technology Innovations · Advanced Computational Techniques and Applications · Advanced Decision-Making Techniques