S Schulz

21 papers receiving 446 citations

Peers

S Schulz
Comparison fields: 5 of 68
  • Pathology and Forensic Medicine 120
  • Rheumatology 79
  • Health Information Management 21
  • Gastroenterology 20
  • Artificial Intelligence 137
Replace Julliette M. Buckley with:
Julliette M. Buckley United States
Kenneth M Feeley United Kingdom
Anushi Shah United States
Noah Weston United States
Xiaoyang Ruan United States
Vivek Pradhan United States
Yue Fan China
Dusan Dj. Popovic Serbia
Ali Dastranj Tabrizi Iran
Wencai Li China
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Citations per field
00.5×5×10×15×19.8×
Julliette M. Buckley · 1×
Citations per year

Countries citing papers authored by S Schulz

Since Specialization
Citations

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

Fields of papers citing papers by S Schulz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside S Schulz, 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 S Schulz Line = papers co-authored together S Schulz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200981
2 201353
3 201552
4 201141
5
Part-whole reasoning in medical ontologies revisited--introducing SEP triplets into classification-based description logics.
199840
6 200932
7
Modeling anatomical spatial relations with description logics.
200027
8
Automated coding of diagnoses--three methods compared.
200025
9 200721
10 200217
11 200816
12 201414
13 200914
14 201014
15
Bidirectional mereological reasoning in anatomical knowledge bases.
200113
16 19986
17 20084
18 20092
19 20171
20 20211

About S Schulz

S Schulz is a scholar working on Pathology and Forensic Medicine, Artificial Intelligence, Molecular Biology, Rheumatology and Medical Terminology, having authored 21 papers that have together received 475 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (7 papers), Natural Language Processing Techniques (5 papers), Semantic Web and Ontologies (5 papers), Systemic Sclerosis and Related Diseases (3 papers), Tuberculosis Research and Epidemiology (2 papers), Mycobacterium research and diagnosis (2 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (2 papers) and Systemic Lupus Erythematosus Research (2 papers). The work is most often cited by research in Pathology and Forensic Medicine (120 citations), Rheumatology (79 citations), Health Information Management (21 citations), Gastroenterology (20 citations) and Artificial Intelligence (137 citations). S Schulz has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Udo Hahn, Chris T. Derk, Ludger Jansen, Martin Romacker, Elizabeth S. Grace, Ingvar Johansson, Sergio A. Rodríguez Jiménez, Rhena F. U. Klar, Catalina Martínez-Costa and Stefania Gallucci. Their work appears in journals such as Yearbook of Medical Informatics, Arthritis & Rheumatology, Lara D. Veeken, Applied Clinical Informatics and Rheumatic Disease Clinics of North America.

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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