Shengbin Jia

1.2k citations
11 papers · 696 indexed · 1 hit paper · h-index 6

Shengbin Jia

10 papers receiving 673 citations

Hit Papers

A review: Knowledge reasoning over knowledge graph5812019202620212023100200300400500

Peers

Shengbin Jia
Comparison fields: 5 of 110
  • Artificial Intelligence 434
  • Management Science and Operations Research 94
  • Information Systems 113
  • Industrial and Manufacturing Engineering 41
  • Health Informatics 5
Replace Xiaojun Chen with:
Xiaojun Chen China
Sabbir M. Rashid United States
Axel Polleres Chile
José Emilio Labra Gayo Chile
Lukas Schmelzeisen Chile
Mohamed Abdel-Basset Egypt
Yufei Wang China
Boris Kovalerchuk United States
Nijat Mehdiyev Germany
Ramzan Talib Pakistan
Shengbin Jia relative to Xiaojun Chen China Xiaojun Chen's profile →
Citations per field
00.5×1.5×2.5×
Xiaojun Chen · 1×
Citations per year

Countries citing papers authored by Shengbin Jia

Since Specialization
Citations

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

Fields of papers citing papers by Shengbin Jia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 14 scholars most cited alongside Shengbin Jia, 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 Shengbin Jia Line = papers co-authored together Shengbin Jia links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 202314
2 20229
3 20215
4 202035
5 20203
6 202012
7
Neural Open Relation Extraction via an Overlap-aware Sequence Tagging Scheme
20191
8 20191
9
A review: Knowledge reasoning over knowledge graphbreakdown →
2019581
10
TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs.
20182
11 201833

About Shengbin Jia

Shengbin Jia is a scholar working on Artificial Intelligence, Management Science and Operations Research and Transportation, having authored 11 papers that have together received 696 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Advanced Graph Neural Networks (4 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (2 papers), Data Quality and Management (2 papers), Plant chemical constituents analysis (1 paper), Sentiment Analysis and Opinion Mining (1 paper) and Biomedical Text Mining and Ontologies (1 paper). The work is most often cited by research in Artificial Intelligence (434 citations), Management Science and Operations Research (94 citations) and Information Systems (113 citations). Shengbin Jia has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Yang Xiang, Xiaojun Chen, Maozhen Li, Yang Li, Ling Ding, Yang Xiang, Xingxing Jiang, Shuping Liu, Hong Shen and Song‐Dong Zhou. Their work appears in journals such as Expert Systems with Applications, Talanta and Journal of Intelligent & Fuzzy Systems.

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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