Ronald J. Brachman

48 papers receiving 3.1k citations

Hit Papers

Readings in Knowledge Representation198520261998201219851985100200300400500

Peers

Ronald J. Brachman
Comparison fields: 5 of 113
  • Artificial Intelligence 2.9k
  • Computer Networks and Communications 936
  • Information Systems 706
  • Computational Theory and Mathematics 380
  • Signal Processing 365
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Countries citing papers authored by Ronald J. Brachman

Since Specialization
Citations

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

Fields of papers citing papers by Ronald J. Brachman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ronald J. Brachman. 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 Ronald J. Brachman. The network helps show where Ronald J. Brachman may publish in the future.

Co-authorship network of co-authors of Ronald J. Brachman

This figure shows the co-authorship network connecting the top 25 collaborators of Ronald J. Brachman. A scholar is included among the top collaborators of Ronald J. Brachman 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 Ronald J. Brachman. Ronald J. Brachman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 3
2 25
3 75
4
The process of knowledge discovery in databases
77
5
The CLASSIC Knowledge Representation System or, KL-ONE: The Next Generation.
11
6
"Reducing" CLASSIC to Practice: Knowledge Representation Theory Meets Reality.
15
7
Metric constraints for maintaining appointments: dates and repeated activities
13
8
Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning
53
9 3
10
The future of knowledge representation
15
11
A knowledge-based software information system
22
12
Vivid knowledge and tractable reasoning
20
13 328
14
The knowledge level of KBMS
2
15 113
16
The tractability of subsumption in frame-based description languages
184
17
KRYPTON: integrating terminology and assertion
32
18 2
19 4
20
Competence in knowledge representation
30

About Ronald J. Brachman

Ronald J. Brachman is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Theory and Mathematics, having authored 49 papers that have together received 3.6k indexed citations. Recurring topics across this work include Semantic Web and Ontologies (21 papers), Logic, Reasoning, and Knowledge (19 papers) and Advanced Database Systems and Queries (11 papers). The work is most often cited by research in Artificial Intelligence (2.9k citations), Computer Networks and Communications (936 citations) and Signal Processing (365 citations). Ronald J. Brachman has collaborated with scholars based in United States, Canada and British Virgin Islands. Frequent co-authors include Hector J. Levesque, James G. Schmolze, Deborah L. McGuinness, Lori Alperin Resnick, Alexander Borgida, Raymond Reiter, Daniele Nardi, Peter F. Patel‐Schneider, James F. Allen and Henry Kautz. Their work appears in journals such as Communications of the ACM, Artificial Intelligence and Cognitive Science.

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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