Jörg Schlötterer

774 total citations · 1 hit paper
25 papers, 339 citations indexed

About

Jörg Schlötterer is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jörg Schlötterer has authored 25 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 10 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jörg Schlötterer's work include Web Data Mining and Analysis (7 papers), Topic Modeling (5 papers) and Information Retrieval and Search Behavior (5 papers). Jörg Schlötterer is often cited by papers focused on Web Data Mining and Analysis (7 papers), Topic Modeling (5 papers) and Information Retrieval and Search Behavior (5 papers). Jörg Schlötterer collaborates with scholars based in Germany, Netherlands and Austria. Jörg Schlötterer's co-authors include Christin Seifert, Meike Nauta, Maurice van Keulen, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michael Granitzer, Folker Meyer, Roman Kern and Hans‐Ulrich Schildhaus and has published in prestigious journals such as ACM Computing Surveys, Information Systems Frontiers and Diagnostics.

In The Last Decade

Jörg Schlötterer

19 papers receiving 328 citations

Hit Papers

From Anecdotal Evidence to Quantitative Evaluation Method... 2023 2026 2024 2025 2023 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
    2023 205 citations Meike Nauta, Jan Trienes et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jörg Schlötterer Germany 7 228 42 42 35 26 25 339
Hanjie Chen United States 6 235 1.0× 28 0.7× 39 0.9× 30 0.9× 11 0.4× 10 347
Ludovik Çoba Italy 5 171 0.8× 28 0.7× 33 0.8× 58 1.7× 14 0.5× 13 267
Hengyi Cai China 9 269 1.2× 62 1.5× 37 0.9× 66 1.9× 10 0.4× 16 401
Eoin M. Kenny Ireland 5 222 1.0× 33 0.8× 36 0.9× 16 0.5× 20 0.8× 10 266
Bernd Malle Austria 4 165 0.7× 39 0.9× 33 0.8× 28 0.8× 6 0.2× 4 266
Nazneen Fatema Rajani United States 10 225 1.0× 57 1.4× 27 0.6× 35 1.0× 11 0.4× 29 315
Milad Moradi Austria 11 274 1.2× 25 0.6× 40 1.0× 44 1.3× 9 0.3× 17 371
Yanghe Pan China 6 123 0.5× 14 0.3× 31 0.7× 45 1.3× 16 0.6× 19 255
Alan Perotti Italy 8 204 0.9× 17 0.4× 74 1.8× 18 0.5× 11 0.4× 19 301
Albert Webson United States 5 342 1.5× 71 1.7× 32 0.8× 46 1.3× 6 0.2× 6 434

Countries citing papers authored by Jörg Schlötterer

Since Specialization
Citations

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

Fields of papers citing papers by Jörg Schlötterer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jörg Schlötterer

This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Schlötterer. A scholar is included among the top collaborators of Jörg Schlötterer 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 Jörg Schlötterer. Jörg Schlötterer 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
1.
2.
Seifert, Christin, et al.. (2024). LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study. 14809–14824. 3 indexed citations
3.
Flek, Lucie, et al.. (2024). Corpus Considerations for Annotator Modeling and Scaling. 1029–1040.
4.
Trienes, Jan, Jörg Schlötterer, Christin Seifert, et al.. (2024). InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification. Universitätsbibliographie, Universität Duisburg-Essen. 4263–4294.
5.
Seifert, Christin, et al.. (2024). CEval: A Benchmark for Evaluating Counterfactual Text Generation. 55–69.
6.
7.
Nauta, Meike, Jan Trienes, Shreyasi Pathak, et al.. (2023). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys. 55(13s). 1–42. 205 indexed citations breakdown →
8.
Nauta, Meike, et al.. (2023). Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges. Universitätsbibliographie, Universität Duisburg-Essen. 6665–6673. 6 indexed citations
9.
Schlötterer, Jörg, et al.. (2023). Privacy-Preserving Knowledge Transfer through Partial Parameter Sharing. Universitätsbibliographie, Universität Duisburg-Essen. 19–23. 1 indexed citations
10.
Trienes, Jan, Jörg Schlötterer, Hans‐Ulrich Schildhaus, & Christin Seifert. (2022). Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset. 19–27. 7 indexed citations
11.
Imangaliyev, Sultan, Jörg Schlötterer, Folker Meyer, & Christin Seifert. (2022). Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data. Diagnostics. 12(10). 2514–2514. 6 indexed citations
12.
Schlötterer, Jörg, et al.. (2022). Model Personalization with Static and Dynamic Patients' Data. 1. 324–333. 1 indexed citations
13.
Granitzer, Michael, et al.. (2021). Query Change as a Contextual Markov Model for Simulating User Search Behaviour. 43–51. 4 indexed citations
14.
Seifert, Christin, et al.. (2017). Ubiquitous Access to Digital Cultural Heritage. Journal on Computing and Cultural Heritage. 10(1). 1–27. 17 indexed citations
15.
Seifert, Christin, Jörg Schlötterer, & Michael Granitzer. (2017). QueryCrumbs: A Compact Visualization for Navigating the Search Query History. University of Twente Research Information. 35–44. 1 indexed citations
16.
Schlötterer, Jörg, et al.. (2016). Inferring semantic interest profiles from Twitter followees. 1152–1157. 10 indexed citations
17.
Schlötterer, Jörg, et al.. (2016). On the quality of semantic interest profiles for onine social network consumers. ACM SIGAPP Applied Computing Review. 16(3). 5–14. 8 indexed citations
18.
Schlötterer, Jörg. (2015). From context to query. 1108–1109. 1 indexed citations
19.
Schlötterer, Jörg, et al.. (2015). A Game with a Purpose to Access Europe's Cultural Treasure.. Data Archiving and Networked Services (DANS). 13–18. 2 indexed citations
20.
Seifert, Christin, Jörg Schlötterer, & Michael Granitzer. (2015). Towards a feature-rich data set for personalized access to long-tail content. Zenodo (CERN European Organization for Nuclear Research). 1031–1038. 3 indexed citations

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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