Knowledge-Based Systems

10.0k papers and 256.4k indexed citations
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About

The 10.0k papers published in Knowledge-Based Systems in the last decades have received a total of 256.4k indexed citations. Papers published in Knowledge-Based Systems usually cover Artificial Intelligence (5.9k papers), Computer Vision and Pattern Recognition (2.5k papers) and Information Systems (1.7k papers) specifically the topics of Topic Modeling (946 papers), Rough Sets and Fuzzy Logic (688 papers) and Face and Expression Recognition (629 papers). The most active scholars publishing in Knowledge-Based Systems are Seyedali Mirjalili, Zeshui Xu, Hamido Fujita, Guiwu Wei, Wen-Tsao Pan, Gaurav Dhiman, Vijay Kumar, Enrique Herrera‐Viedma, Francisco Chiclana and Jesús Bobadilla.

In The Last Decade

Knowledge-Based Systems

8.9k papers receiving 237.8k citations

Fields of papers published in Knowledge-Based Systems

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Knowledge-Based Systems. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Knowledge-Based Systems.

Countries where authors publish in Knowledge-Based Systems

Since Specialization
Citations

This map shows the geographic impact of research published in Knowledge-Based Systems. 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 papers published in Knowledge-Based Systems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Knowledge-Based Systems more than expected).

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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