Data & Knowledge Engineering

1.9k papers and 30.6k indexed citations i.

About

The 1.9k papers published in Data & Knowledge Engineering in the last decades have received a total of 30.6k indexed citations. Papers published in Data & Knowledge Engineering usually cover Artificial Intelligence (1.2k papers), Computer Networks and Communications (868 papers) and Information Systems (726 papers) specifically the topics of Advanced Database Systems and Queries (679 papers), Semantic Web and Ontologies (594 papers) and Data Management and Algorithms (531 papers). The most active scholars publishing in Data & Knowledge Engineering are Rudi Studer, Dieter Fensel, V. Richard Benjamins, Derya Birant, Alp Kut, Wil M. P. van der Aalst, Veda C. Storey, Manfred Reichert, Lipika Dey and Amir Ahmad.

In The Last Decade

Fields of papers published in Data & Knowledge Engineering

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Data & Knowledge Engineering. 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 Data & Knowledge Engineering.

Countries where authors publish in Data & Knowledge Engineering

Since Specialization
Citations

This map shows the geographic impact of research published in Data & Knowledge Engineering. 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 Data & Knowledge Engineering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Data & Knowledge Engineering 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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2025