Jörg Gebhardt

1.1k citations
31 papers · 365 · h-index 10

Impact in

Papers in

Jörg Gebhardt

27 papers receiving 334 citations

Peers

Jörg Gebhardt
Comparison fields: 5 of 82
  • Management Science and Operations Research 66
  • Artificial Intelligence 152
  • Industrial and Manufacturing Engineering 40
  • Medical Laboratory Technology 6
  • Computational Theory and Mathematics 62
Replace Volker Lohweg with:
Volker Lohweg Germany
An‐Da Li China
André Lemos Brazil
Daqing Wu China
Biao Xu China
Moncef Tagina Tunisia
G.A. Vijayalakshmi Pai India
Lie Meng Pang China
Hong Choon Ong Malaysia
Jörg Gebhardt relative to Volker Lohweg Germany Volker Lohweg's profile →
Citations per field
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Citations per year

Countries citing papers authored by Jörg Gebhardt

Since Specialization
Citations

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

Fields of papers citing papers by Jörg Gebhardt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Jörg Gebhardt, 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 Jörg Gebhardt Line = papers co-authored together Jörg Gebhardt links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202069
2
European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty.
199456
3 199440
4 199534
5 199328
6 202126
7 199523
8 200817
9 20199
10 20219
11 20216
12 20195
13 20205
14 20024
15 20034
16 20064
17 19943
18 20043
19 20163
20 20053

About Jörg Gebhardt

Jörg Gebhardt is a scholar working on Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research, Electrical and Electronic Engineering and Biomedical Engineering, having authored 31 papers that have together received 365 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (8 papers), Multi-Criteria Decision Making (5 papers), Fuzzy Logic and Control Systems (4 papers), AI-based Problem Solving and Planning (3 papers), Advanced Sensor Technologies Research (3 papers), Rough Sets and Fuzzy Logic (3 papers), Advanced Chemical Sensor Technologies (2 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Management Science and Operations Research (66 citations), Artificial Intelligence (152 citations), Industrial and Manufacturing Engineering (40 citations), Medical Laboratory Technology (6 citations) and Computational Theory and Mathematics (62 citations). Jörg Gebhardt has collaborated with scholars based in Germany, Switzerland and India. Frequent co-authors include Rudolf Kruse, Frank Klawonn, Rainer Palm, S. Wildermuth, Stefan Panglisch, R. Gimbel, Daniel Beverungen, Thomas Leibfried, Volker Stich and Ralf Gitzel. Their work appears in journals such as Sensors, International Journal of Approximate Reasoning, IEEE Transactions on Fuzzy Systems, Frontiers in Medicine and Journal of Water Supply Research and Technology—AQUA.

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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