Jörg H. Siekmann

2.0k citations
40 papers · 575 indexed · h-index 11

Jörg H. Siekmann

36 papers receiving 504 citations

Peers

Jörg H. Siekmann
Comparison fields: 5 of 74
  • Computational Theory and Mathematics 311
  • Artificial Intelligence 484
  • Software 32
  • Computer Networks and Communications 84
  • Theoretical Computer Science 4
Replace Tudor Jebelean with:
Tudor Jebelean Austria
Heribert Vollmer Germany
Benjamin Rossman United States
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Eric Mendelsohn Canada
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C. R. Murthy United States
Judicaël Courant France
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Jörg H. Siekmann relative to Tudor Jebelean Austria Tudor Jebelean's profile →
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Countries citing papers authored by Jörg H. Siekmann

Since Specialization
Citations

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

Fields of papers citing papers by Jörg H. Siekmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jörg H. Siekmann, 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 H. Siekmann Line = papers co-authored together Jörg H. Siekmann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Resource-Bounded Modelling and Analysis of Human-Level Interactive Proofs.
20110
2 20074
3 200525
4
Holonic Multiagent Systems: A Foundation for the Organisation of Multiagent Systems
20031
5
KI - Die Entwicklung der Disziplin in Deutschland.
20022
6
Proof Development with Omega-MEGA: sqrt(2) Is Irrational
200210
7 20011
8 199932
9
An Interactive Proof Development Environment + Anticipation = A Mathematical Assistant?
19982
10 19982
11 199241
12 1989115
13
Abelian semigroups
19875
14
Unification theory
19866
15 198610
16
Classical papers on computational logic
19831
17 19839
18 198260
19
The Markgraf Karl refutation procedure
198113
20
Universal unification and regular equational ACFM theories
19812

About Jörg H. Siekmann

Jörg H. Siekmann is a scholar working on Computational Theory and Mathematics, Theoretical Computer Science and Artificial Intelligence, having authored 40 papers that have together received 575 indexed citations. Recurring topics across this work include Logic, programming, and type systems (15 papers), Logic, Reasoning, and Knowledge (10 papers), semigroups and automata theory (5 papers), Semantic Web and Ontologies (5 papers), Advanced Algebra and Logic (4 papers), Formal Methods in Verification (4 papers), AI-based Problem Solving and Planning (3 papers) and Computability, Logic, AI Algorithms (2 papers). The work is most often cited by research in Computational Theory and Mathematics (311 citations), Artificial Intelligence (484 citations) and Software (32 citations). Jörg H. Siekmann has collaborated with scholars based in Germany, United States and Canada. Frequent co-authors include Franz Baader, Erica Melis⋆, Lotfi A. Zadeh, Leszek Rutkowski, Christoph Benzmüller, Christoph Beierle, Peter H. Schmitt, Serge Autexier, Hans Jürgen Ohlbach and Andreas Meier. Their work appears in journals such as Artificial Intelligence, Journal of the ACM and Decision Support 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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