Henk Harkema

994 total citations
22 papers, 646 citations indexed

About

Henk Harkema is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Henk Harkema has authored 22 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 15 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Henk Harkema's work include Biomedical Text Mining and Ontologies (15 papers), Natural Language Processing Techniques (13 papers) and Topic Modeling (9 papers). Henk Harkema is often cited by papers focused on Biomedical Text Mining and Ontologies (15 papers), Natural Language Processing Techniques (13 papers) and Topic Modeling (9 papers). Henk Harkema collaborates with scholars based in United States, United Kingdom and Vietnam. Henk Harkema's co-authors include Wendy W. Chapman, John Dowling, Melissa Saul, Evan S. Dellon, Robert E. Schoen, Ateev Mehrotra, Robert Gaizauskas, Danielle L. Mowery, Richard D. Boyce and Yikun Guo and has published in prestigious journals such as Gastrointestinal Endoscopy, Journal of the American Medical Informatics Association and Journal of Biomedical Informatics.

In The Last Decade

Henk Harkema

21 papers receiving 600 citations

Peers

Henk Harkema
Comparison fields: 5 of 76
  • Artificial Intelligence 401
  • Molecular Biology 339
  • Oncology 106
  • Pulmonary and Respiratory Medicine 81
  • Health Information Management 76
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Citations per field, relative to Henk Harkema
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Citations per year, relative to Henk Harkema
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Countries citing papers authored by Henk Harkema

Since Specialization
Citations

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

Fields of papers citing papers by Henk Harkema

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Henk Harkema. 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 Henk Harkema. The network helps show where Henk Harkema may publish in the future.

Co-authorship network of co-authors of Henk Harkema

This figure shows the co-authorship network connecting the top 25 collaborators of Henk Harkema. A scholar is included among the top collaborators of Henk Harkema 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 Henk Harkema. Henk Harkema 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
# Work Indexed citations
1 0
2 32
3
Semantic annotation of clinical events for generating a problem list.
13
4
Using Natural Language Processing to Extract Drug-Drug Interaction Information from Package Inserts
9
5 72
6
Using natural language processing to identify pharmacokinetic drug-drug interactions described in drug package inserts
20
7 25
8 76
9 3
10 274
11
Methodology to develop and evaluate a semantic representation for NLP.
9
12 7
13 8
14
The University of Sheffield's TREC 2005 Q&A Experiments.
19
15
Automatically acquiring a linguistically motivated genic interaction extraction system
10
16 1
17
Sheffield University and the TREC 2004 Genomics Track: Query Expansion Using Synonymous Terms.
19
18
A Large-Scale Resource for Storing and Recognizing Technical Terminology.
4
19
A Large Scale Terminology Resource for Biomedical Text Processing
14
20
A Recognizer for Minimalist Grammars.
15

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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