Natalia Grabar

1.4k citations
122 papers · 735 · h-index 14

Impact in

Papers in

    • Natural Language Processing Techniques 68
    • Topic Modeling 37
    • Semantic Web and Ontologies 20
    • Text Readability and Simplification 19
    • Biomedical Text Mining and Ontologies 60

Natalia Grabar

108 papers receiving 654 citations

Peers

Natalia Grabar
Comparison fields: 5 of 105
  • Artificial Intelligence 478
  • Health Information Management 63
  • Toxicology 37
  • Language and Linguistics 90
  • Molecular Biology 332
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Citations per year

Countries citing papers authored by Natalia Grabar

Since Specialization
Citations

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

Fields of papers citing papers by Natalia Grabar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Natalia Grabar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Natalia Grabar Line = papers co-authored together Natalia Grabar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 122 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201050
2 201040
3 200430
4
Machine learning approach for automatic quality criteria detection of health web pages.
200726
5 200326
6 201323
7 201022
8
Building a text corpus for representing the variety of medical language.
200121
9 201220
10 201618
11
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
201216
12 201815
13 202013
14
The contribution of morphological knowledge to French MeSH mapping for information retrieval.
200113
15 200212
16 201212
17
A general method for sifting linguistic knowledge from structured terminologies.
200012
18
Acquisition automatique de connaissances morphologiques sur le vocabulaire médical
199911
19 201810
20 202110

About Natalia Grabar

Natalia Grabar is a scholar working on Artificial Intelligence, Molecular Biology, Language and Linguistics, Philosophy and Toxicology, having authored 122 papers that have together received 735 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (68 papers), Biomedical Text Mining and Ontologies (60 papers), Topic Modeling (37 papers), linguistics and terminology studies (23 papers), Semantic Web and Ontologies (20 papers), Text Readability and Simplification (19 papers), Linguistics and Discourse Analysis (11 papers) and Pharmacovigilance and Adverse Drug Reactions (9 papers). The work is most often cited by research in Artificial Intelligence (478 citations), Health Information Management (63 citations), Toxicology (37 citations), Language and Linguistics (90 citations) and Molecular Biology (332 citations). Natalia Grabar has collaborated with scholars based in France, Belgium and Japan. Frequent co-authors include Thierry Hamon, Pierre Zweigenbaum, Célia Boyer, Éric Zapletal, Stéfan Darmoni, Patrice Degoulet, Fleur Mougin, Arnaud Gaudinat, Cyril Grouin and Vincent Claveau. Their work appears in journals such as Yearbook of Medical Informatics, Terminology International Journal of Theoretical and Applied Issues in Specialized Communication, Journal of the American Medical Informatics Association, International Journal of Medical Informatics and Journal of Biomedical Semantics.

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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