Nataša Milić-Frayling

3.3k total citations · 1 hit paper
82 papers, 2.1k citations indexed

About

Nataša Milić-Frayling is a scholar working on Artificial Intelligence, Information Systems and Human-Computer Interaction. According to data from OpenAlex, Nataša Milić-Frayling has authored 82 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 32 papers in Information Systems and 15 papers in Human-Computer Interaction. Recurrent topics in Nataša Milić-Frayling's work include Topic Modeling (17 papers), Natural Language Processing Techniques (12 papers) and Usability and User Interface Design (11 papers). Nataša Milić-Frayling is often cited by papers focused on Topic Modeling (17 papers), Natural Language Processing Techniques (12 papers) and Usability and User Interface Design (11 papers). Nataša Milić-Frayling collaborates with scholars based in United Kingdom, United States and Slovenia. Nataša Milić-Frayling's co-authors include Gabriella Kazai, Jaap Kamps, Eduarda Mendes Rodrigues, Marko Grobelnik, Marc A. Smith, Janez Brank, Jukka Riekki, Mika Rautiainen, Ben Shneiderman and Aaron Quigley and has published in prestigious journals such as Proceedings of the VLDB Endowment, Lecture notes in computer science and International Journal of Human-Computer Interaction.

In The Last Decade

Nataša Milić-Frayling

80 papers receiving 2.0k citations

Hit Papers

Lecture Notes in Computer... 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nataša Milić-Frayling United Kingdom 20 812 618 426 359 245 82 2.1k
Geert‐Jan Houben Netherlands 27 1.0k 1.3× 1.0k 1.7× 572 1.3× 262 0.7× 460 1.9× 126 2.7k
Alessandro Bozzon Netherlands 23 861 1.1× 683 1.1× 496 1.2× 227 0.6× 217 0.9× 145 1.9k
Fu Lee Wang Hong Kong 32 1.4k 1.7× 762 1.2× 311 0.7× 654 1.8× 190 0.8× 243 3.4k
Jilin Chen United States 21 1.1k 1.3× 1.1k 1.8× 304 0.7× 373 1.0× 452 1.8× 64 2.6k
George D. Magoulas United Kingdom 28 1.1k 1.4× 452 0.7× 584 1.4× 366 1.0× 142 0.6× 145 2.8k
Tsvi Kuflik Israel 29 1.0k 1.3× 1.1k 1.8× 187 0.4× 876 2.4× 423 1.7× 209 3.0k
Changqin Huang China 32 1.0k 1.2× 758 1.2× 465 1.1× 501 1.4× 157 0.6× 143 3.2k
Myra Spiliopoulou Germany 27 1.2k 1.5× 1.2k 1.9× 108 0.3× 332 0.9× 223 0.9× 206 3.1k
Ruimin Shen China 24 698 0.9× 804 1.3× 282 0.7× 572 1.6× 286 1.2× 115 2.5k
Ben Shneiderman United States 23 1.2k 1.5× 1.0k 1.7× 298 0.7× 1.2k 3.2× 376 1.5× 63 3.9k

Countries citing papers authored by Nataša Milić-Frayling

Since Specialization
Citations

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

Fields of papers citing papers by Nataša Milić-Frayling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nataša Milić-Frayling. 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 Nataša Milić-Frayling. The network helps show where Nataša Milić-Frayling may publish in the future.

Co-authorship network of co-authors of Nataša Milić-Frayling

This figure shows the co-authorship network connecting the top 25 collaborators of Nataša Milić-Frayling. A scholar is included among the top collaborators of Nataša Milić-Frayling 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 Nataša Milić-Frayling. Nataša Milić-Frayling 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.
Zheng, Wen, Nataša Milić-Frayling, & Ke Zhou. (2020). Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation. 3581–3591. 5 indexed citations
2.
Raza, Mohammad, Sumit Gulwani, & Nataša Milić-Frayling. (2015). Compositional program synthesis from natural language and examples. International Conference on Artificial Intelligence. 792–800. 26 indexed citations
3.
Rodrigues, Eduarda Mendes, et al.. (2013). Network analysis of third party tracking: user exposure to tracking cookies through search. ePrints Soton (University of Southampton). 1 indexed citations
4.
Rautiainen, Mika, Timo Korhonen, Edward Mutafungwa, et al.. (2012). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture notes in computer science. 459 indexed citations breakdown →
6.
Kazai, Gabriella & Nataša Milić-Frayling. (2009). On the Evaluation of the Quality of Relevance Assessments Collected through Crowdsourcing. 13 indexed citations
7.
Vinay, Vishwa, et al.. (2009). Measuring system performance and topic discernment using generalized adaptive-weight mean. 2033–2036. 1 indexed citations
8.
Kazai, Gabriella & Nataša Milić-Frayling. (2009). Effects of Social Approval Votes on Search Performance. 2. 1554–1559. 8 indexed citations
9.
Kantor, Paul B., Gabriella Kazai, Nataša Milić-Frayling, & Ross Wilkinson. (2008). Proceedings of the 2008 ACM workshop on Research advances in large digital book repositories. 1 indexed citations
10.
Kazai, Gabriella & Nataša Milić-Frayling. (2008). Trust, authority and popularity in social information retrieval. 1503–1504. 12 indexed citations
11.
Brank, Janez, Dunja Mladenić, Marko Grobelnik, & Nataša Milić-Frayling. (2008). Feature Selection for the Classification of Large Document Collections. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations
12.
Leskovec, Jure, Nataša Milić-Frayling, & Marko Grobelnik. (2005). Impact of linguistic analysis on the semantic graph coverage and learning of document extracts. National Conference on Artificial Intelligence. 1069–1074. 27 indexed citations
13.
Vinay, Vishwa, Ken Wood, Nataša Milić-Frayling, & Ingemar J. Cox. (2005). Comparing relevance feedback algorithms for web search. 1052–1052. 15 indexed citations
14.
Leskovec, Jure, Marko Grobelnik, & Nataša Milić-Frayling. (2004). Learning Sub-Structures of Document Semantic Graphs for Document Summarization. Zenodo (CERN European Organization for Nuclear Research). 48 indexed citations
15.
Milić-Frayling, Nataša, et al.. (2004). Smartback. 63–71. 50 indexed citations
16.
Brank, Janez, Nataša Milić-Frayling, & Marko Grobelnik. (2003). Training text classifiers with SVM on very few positive examples. 71(2). 27–13. 26 indexed citations
17.
Brank, Janez, Marko Grobelnik, Nataša Milić-Frayling, & Dunja Mladenić. (2003). Sparsity analysis of term weighting schemes: Application to Feature Selection. 10. 1 indexed citations
18.
Zhai, ChengXiang, et al.. (1996). Experiments on chinese text indexing : CLARIT TREC-5 Chinese track report. Text REtrieval Conference. 335–339. 5 indexed citations
19.
Milić-Frayling, Nataša, et al.. (1996). CLARIT compound queries and constraint-controlled feedback in TREC-5 Ad-Hoc experiments. Text REtrieval Conference. 315–334. 2 indexed citations
20.
Zhai, ChengXiang, et al.. (1996). OCR correction and query expansion for retrieval on OCR data : CLARIT TREC-5 confusion track report. Text REtrieval Conference. 341–345. 11 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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