Ming-Wen Shao

537 total citations
13 papers, 438 citations indexed

About

Ming-Wen Shao is a scholar working on Computational Theory and Mathematics, Information Systems and Artificial Intelligence. According to data from OpenAlex, Ming-Wen Shao has authored 13 papers receiving a total of 438 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Theory and Mathematics, 9 papers in Information Systems and 7 papers in Artificial Intelligence. Recurrent topics in Ming-Wen Shao's work include Rough Sets and Fuzzy Logic (12 papers), Data Mining Algorithms and Applications (9 papers) and Semantic Web and Ontologies (3 papers). Ming-Wen Shao is often cited by papers focused on Rough Sets and Fuzzy Logic (12 papers), Data Mining Algorithms and Applications (9 papers) and Semantic Web and Ontologies (3 papers). Ming-Wen Shao collaborates with scholars based in China and Hong Kong. Ming-Wen Shao's co-authors include Changzhong Wang, Xiaodong Fan, Yee Leung, Wen‐Xiu Zhang, Cheng Wu, Min Liu, Yang Huang, Degang Chen, Wei-Zhi Wu and Xizhao Wang and has published in prestigious journals such as Fuzzy Sets and Systems, Knowledge-Based Systems and Computers & Mathematics with Applications.

In The Last Decade

Ming-Wen Shao

13 papers receiving 429 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ming-Wen Shao China	7	408	242	231	79	66	13	438
Jian-Min Ma China	14	548 1.3×	257 1.1×	285 1.2×	147 1.9×	72 1.1×	20	581
Ma Xiao China	9	322 0.8×	174 0.7×	209 0.9×	96 1.2×	30 0.5×	26	392
Zhehuang Huang China	10	301 0.7×	164 0.7×	159 0.7×	89 1.1×	39 0.6×	22	367
Xiaofei Deng Canada	7	447 1.1×	143 0.6×	227 1.0×	238 3.0×	53 0.8×	10	517
LV Yue-jin China	7	539 1.3×	270 1.1×	323 1.4×	176 2.2×	116 1.8×	37	609
Xiaoli He China	9	317 0.8×	127 0.5×	132 0.6×	106 1.3×	36 0.5×	26	347
Shaoyong Li China	7	308 0.8×	206 0.9×	110 0.5×	76 1.0×	79 1.2×	15	319
Yunsong Qi China	9	228 0.6×	116 0.5×	201 0.9×	71 0.9×	18 0.3×	16	368
Zbigniew Bonikowski Poland	3	421 1.0×	133 0.5×	144 0.6×	103 1.3×	31 0.5×	4	431

Countries citing papers authored by Ming-Wen Shao

Since Specialization

Citations

This map shows the geographic impact of Ming-Wen Shao's research. 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 Ming-Wen Shao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Wen Shao more than expected).

Fields of papers citing papers by Ming-Wen Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Wen Shao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming-Wen Shao. The network helps show where Ming-Wen Shao may publish in the future.

Co-authorship network of co-authors of Ming-Wen Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Wen Shao. A scholar is included among the top collaborators of Ming-Wen Shao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming-Wen Shao. Ming-Wen Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Shao, Ming-Wen, et al.. (2024). Uncertainty-Aware Diffusion Model for Real-World Image Dehazing. 65–70. 1 indexed citations

2.

Shao, Ming-Wen, et al.. (2021). Cognitive Computing and Rule Extraction in Generalized One-sided Formal Contexts. Cognitive Computation. 14(6). 2087–2107. 6 indexed citations

3.

Wang, Changzhong, Yang Huang, Ming-Wen Shao, & Degang Chen. (2018). Uncertainty measures for general fuzzy relations. Fuzzy Sets and Systems. 360. 82–96. 69 indexed citations

4.

Wang, Changzhong, et al.. (2018). Attribute reduction based on k-nearest neighborhood rough sets. International Journal of Approximate Reasoning. 106. 18–31. 151 indexed citations

5.

Shao, Ming-Wen, Yee Leung, Xizhao Wang, & Wei-Zhi Wu. (2016). Granular reducts of formal fuzzy contexts. Knowledge-Based Systems. 114. 156–166. 35 indexed citations

6.

Shao, Ming-Wen & Yee Leung. (2014). Relations between granular reduct and dominance reduct in formal contexts. Knowledge-Based Systems. 65. 1–11. 41 indexed citations

7.

Shao, Ming-Wen & Hongzhi Yang. (2012). Two kinds of multi-level formal concepts and its application for sets approximations. International Journal of Machine Learning and Cybernetics. 4(6). 621–630. 9 indexed citations

8.

Shao, Ming-Wen, et al.. (2009). Maximal Dominance Link technique for knowledge aquisition in ordered information system. 20. 488–492. 1 indexed citations

9.

Yang, Hongzhi & Ming-Wen Shao. (2008). Attribute characters of formal contexts under homomorphisms. 724–729. 1 indexed citations

10.

Liu, Min, Ming-Wen Shao, Wen‐Xiu Zhang, & Cheng Wu. (2007). Reduction method for concept lattices based on rough set theory and its application. Computers & Mathematics with Applications. 53(9). 1390–1410. 108 indexed citations

11.

Shao, Ming-Wen & Wen‐Xiu Zhang. (2007). Information Granularity Lattices. 3728–3733. 4 indexed citations

12.

Shao, Ming-Wen. (2007). Knowledge Acquisition in Decision Formal Contexts. 4050–4054. 8 indexed citations

13.

Shao, Ming-Wen. (2005). The reduction for two kind of generalized concept lattices. 1867. 2217–2222 Vol. 4. 4 indexed 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.

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