Zhentao Liang

dblp:261/9577 · DBLP profile ↗
← Back
4ranked-venue papers
3as first author
4since 2021 · last 2026
0000-0003-2927-3523ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 4 · 3 first-author · 4 since 2021
YearPublicationVenuePosition
2026 Citation importance-aware document representation learning for large-scale science mapping
Zhentao Liang, Nees Jan van Eck, Xuehua Wu, Jin Mao 0001, Gang Li 0040
Inf. Process. Manag.1
2023 Bias against scientific novelty: A prepublication perspective
abstract
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.
Zhentao Liang, Jin Mao 0001, Gang Li 0040
J. Assoc. Inf. Sci. Technol.1
2023 Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities
abstract
Abstract Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity‐based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co‐occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co‐occurrences outperforms that based on MeSH terms and three earlier citation‐based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.
Yaxue Ma, Jin Mao 0001, Zhentao Liang, Gang Li 0040
J. Assoc. Inf. Sci. Technol.5
2021 Combining deep neural network and bibliometric indicator for emerging research topic prediction
Zhentao Liang, Jin Mao 0001, Kun Lu 0001, Zhichao Ba, Gang Li 0040
Inf. Process. Manag.1