Gong Cheng

2.8k citations
109 papers · 1.3k indexed · h-index 17

Gong Cheng

101 papers receiving 1.2k citations

Peers

Gong Cheng
Comparison fields: 5 of 107
  • Artificial Intelligence 971
  • Information Systems 522
  • Management Science and Operations Research 277
  • Signal Processing 137
  • Computer Networks and Communications 203
Replace Dae-Ki Kang with:
Dae-Ki Kang South Korea
Mohammed Alweshah Jordan
Mohd Zakree Ahmad Nazri Malaysia
Zalinda Othman Malaysia
Ladjel Bellatreche France
Ezz El‐Din Hemdan Egypt
Jingbo Shang United States
Honghui Xu United States
Mohd Fadzil Hassan Malaysia
Satvik Vats India
Gong Cheng relative to Dae-Ki Kang South Korea Dae-Ki Kang's profile →
Citations per field
00.5×3.1×
Dae-Ki Kang · 1×
Citations per year

Countries citing papers authored by Gong Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Gong Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gong Cheng. 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 Gong Cheng. The network helps show where Gong Cheng may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Gong Cheng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Gong Cheng Line = papers co-authored together Gong Cheng links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20253
4 20250
5 20241
6 20241
7 20240
8 20243
9 202027
10
ESSTER at the EYRE 2020 Entity Summarization Task.
20203
11 20194
12
ES-LDA: Entity Summarization using Knowledge-based Topic Modeling
201717
13 201755
14
HIEDS: a generic and efficient approach to hierarchical dataset summarization
20166
15
Taking up the gaokao challenge: an information retrieval approach
201611
16
RelClus: clustering-based relationship search
20131
17
Research on Framework of Virtual Simulation System of Nuclear Facilities Decommissioning
20113
18
Development of a Network Expert System for Diagnosis and Control of Silkworm Diseases
20091
19 200711
20 20063

About Gong Cheng

Gong Cheng is a scholar working on Artificial Intelligence, Management Science and Operations Research and Signal Processing, having authored 109 papers that have together received 1.3k indexed citations. Recurring topics across this work include Semantic Web and Ontologies (46 papers), Topic Modeling (27 papers), Data Quality and Management (22 papers), Natural Language Processing Techniques (20 papers), Data Management and Algorithms (16 papers), Web Data Mining and Analysis (14 papers), Service-Oriented Architecture and Web Services (14 papers) and Advanced Graph Neural Networks (12 papers). The work is most often cited by research in Artificial Intelligence (971 citations), Information Systems (522 citations) and Management Science and Operations Research (277 citations). Gong Cheng has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Yuzhong Qu, Wei Hu, Weiyi Ge, Xiang Zhang, D. W. Herrin, Yawei Sun, Evgeny Kharlamov, Lingling Zhang, Honghan Wu and Tingting Zhu. Their work appears in journals such as Nature, SHILAP Revista de lepidopterología and Bioresource Technology.

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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