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Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023)
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- Date
- 22. 3.2023 12:06:55
- Language
- e
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Yu, C.; Xue, H.; An, L.; Li, G.: ¬A lightweight semantic-enhanced interactive network for efficient short-text matching (2023)
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- Abstract
- Knowledge-enhanced short-text matching has been a significant task attracting much attention in recent years. However, the existing approaches cannot effectively balance effect and efficiency. Effective models usually consist of complex network structures leading to slow inference speed and the difficulties of applications in actual practice. In addition, most knowledge-enhanced models try to link the mentions in the text to the entities of the knowledge graphs-the difficulties of entity linking decrease the generalizability among different datasets. To address these problems, we propose a lightweight Semantic-Enhanced Interactive Network (SEIN) model for efficient short-text matching. Unlike most current research, SEIN employs an unsupervised method to select WordNet's most appropriate paraphrase description as the external semantic knowledge. It focuses on integrating semantic information and interactive information of text while simplifying the structure of other modules. We conduct intensive experiments on four real-world datasets, that is, Quora, Twitter-URL, SciTail, and SICK-E. Compared with state-of-the-art methods, SEIN achieves the best performance on most datasets. The experimental results proved that introducing external knowledge could effectively improve the performance of the short-text matching models. The research sheds light on the role of lightweight models in leveraging external knowledge to improve the effect of short-text matching.
- Date
- 22. 1.2023 19:05:27
- Language
- e
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Liang, Z.; Mao, J.; Li, G.: Bias against scientific novelty : a prepublication perspective (2023)
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- Abstract
- Novel ideas often experience resistance from incumbent forces. While evidence of the bias against novelty has been widely identified in science, there is still a lack of large-scale quantitative work to study this problem occurring in the prepublication process of manuscripts. This paper examines the association between manuscript novelty and handling time of publication based on 778,345 articles in 1,159 journals indexed by PubMed. Measuring the novelty as the extent to which manuscripts disrupt existing knowledge, we found systematic evidence that higher novelty is associated with longer handling time. Matching and fixed-effect models were adopted to confirm the statistical significance of this pattern. Moreover, submissions from prestigious authors and institutions have the advantage of shorter handling time, but this advantage is diminishing as manuscript novelty increases. In addition, we found longer handling time is negatively related to the impact of manuscripts, while the relationships between novelty and 3- and 5-year citations are U-shape. This study expands the existing knowledge of the novelty bias by examining its existence in the prepublication process of manuscripts.
- Language
- e
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Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023)
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- Date
- 22. 1.2023 18:37:33
- Language
- e
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Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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