Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Nonmonotonic reasoning, preferential models and cumulative logics
1990793 citationsSarit Kraus, Daniel Lehmann et al.profile →
Countries citing papers authored by Daniel Lehmann
Since
Specialization
Citations
This map shows the geographic impact of Daniel Lehmann'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 Daniel Lehmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Lehmann more than expected).
This network shows the impact of papers produced by Daniel Lehmann. 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 Daniel Lehmann. The network helps show where Daniel Lehmann may publish in the future.
Co-authorship network of co-authors of Daniel Lehmann
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Lehmann.
A scholar is included among the top collaborators of Daniel Lehmann 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 Daniel Lehmann. Daniel Lehmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lehmann, Daniel, et al.. (1998). Tuning a Neural Network for Harmonizing Melodies in Real-Time. The Journal of the Abraham Lincoln Association. 1998.10 indexed citations
8.
Lehmann, Daniel, et al.. (1997). Harmonizing Melodies in Real-Time: the Connectionist Approach. The Journal of the Abraham Lincoln Association. 1997. 27–30.8 indexed citations
9.
Goldman, Claudia V., et al.. (1996). NetNeg: A Hybrid Interactive Architecture for Composing Polyphonic Music in Real Time. The Journal of the Abraham Lincoln Association. 1996. 133–140.3 indexed citations
10.
Schlechta, Karl, Daniel Lehmann, & Menachem Magidor. (1996). Distance Semantics for Belief Revision. arXiv (Cornell University). 137–145.8 indexed citations
11.
Lehmann, Daniel. (1995). Belief revision, revised. International Joint Conference on Artificial Intelligence. 1534–1540.85 indexed citations
12.
Lehmann, Daniel, et al.. (1995). An Artificial Neural Net for Harmonizing Melodies.. The Journal of the Abraham Lincoln Association. 1995.3 indexed citations
Rabin, Michael O. & Daniel Lehmann. (1994). The advantages of free choice: a symmetric and fully distributed solution for the dining philosophers problem. 333–352.14 indexed citations
15.
Lehmann, Daniel & Menachem Magidor. (1990). Preferential logics: the predicate calculus case. 57–72.13 indexed citations
16.
Kraus, Sarit & Daniel Lehmann. (1988). Knowledge, belief and time. Theoretical Computer Science. 58(1-3). 155–174.62 indexed citations
17.
Lehmann, Daniel. (1982). Géométrie et topologie des surfaces. Presses Universitaires de France eBooks.1 indexed citations
18.
Lehmann, Daniel & Saharon Shelah. (1982). Reasoning with time and chance. Information and Control. 53(3). 165–198.70 indexed citations
19.
Lehmann, Daniel. (1977). Théorie homotopique des formes différentielles (d'après D. Sullivan). French digital mathematics library (Numdam).9 indexed citations
20.
Lehmann, Daniel & Michael Smyth. (1977). Data Types (Extended Abstract). 32. 7–12.3 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.