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.
The DLV system for knowledge representation and reasoning
2006501 citationsNicola Leone, Gerald Pfeifer et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Gerald Pfeifer
Since
Specialization
Citations
This map shows the geographic impact of Gerald Pfeifer'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 Gerald Pfeifer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerald Pfeifer more than expected).
This network shows the impact of papers produced by Gerald Pfeifer. 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 Gerald Pfeifer. The network helps show where Gerald Pfeifer may publish in the future.
Co-authorship network of co-authors of Gerald Pfeifer
This figure shows the co-authorship network connecting the top 25 collaborators of Gerald Pfeifer.
A scholar is included among the top collaborators of Gerald Pfeifer 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 Gerald Pfeifer. Gerald Pfeifer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eiter, Thomas, Wolfgang Faber, Michael Fink, Gerald Pfeifer, & Stefan Woltran. (2004). Complexity of model checking and bounded predicate arities for non-ground answer set programming. Principles of Knowledge Representation and Reasoning. 377–387.7 indexed citations
4.
Faber, Wolfgang, et al.. (2003). Aggregate functions in disjunctive logic programming: semantics, complexity, and implementation in DLV. International Joint Conference on Artificial Intelligence. 847–852.46 indexed citations
Calimeri, Francesco, Wolfgang Faber, Nicola Leone, Simona Perri, & Gerald Pfeifer. (2001). DLV - Declarative Problem Solving Using Answer Set Programming.1 indexed citations
12.
Faber, Wolfgang, Nicola Leone, & Gerald Pfeifer. (2001). Experimenting with heuristics for answer set programming. International Joint Conference on Artificial Intelligence. 635–640.24 indexed citations
Eiter, Thomas, Wolfgang Faber, Nicola Leone, Gerald Pfeifer, & Axel Polleres. (2000). Using the dlv System for Planning and Diagnostic Reasoning.. 125–134.3 indexed citations
15.
Eiter, Thomas, Wolfgang Faber, Nicola Leone, & Gerald Pfeifer. (1999). The Diagnosis Frontend of the dlv system. AI Communications. 12(1). 99–111.38 indexed citations
Eiter, Thomas, Nicola Leone, Cristinel Mateis, Gerald Pfeifer, & Francesco Scarcello. (1998). The KR system dlv: progress report, comparisons and benchmarks. Principles of Knowledge Representation and Reasoning. 406–417.100 indexed citations
18.
Eiter, Thomas, Nicola Leone, Cristinel Mateis, Gerald Pfeifer, & Francesco Scarcello. (1997). The Architecture of a Disjunctive Deductive Database System.. 141–152.2 indexed citations
Eiter, Thomas, Wolfgang Faber, Georg Gottlob, et al.. (1997). The dlv System: Model Generator and Application Frontends.24 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.