Bojan Cestnik
- Artificial Intelligence top 2%
- Information Systems top 2%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Ivan BratkoIgor KononenkoNada LavračPeter FlachDragan GambergerTanja UrbančičMarko BohanecSašo Džeroski
- Topics
- Biomedical Text Mining and Ontologies (11 papers)Advanced Text Analysis Techniques (7 papers)Topic Modeling (5 papers)
In The Last Decade
Bojan Cestnik
37 papers receiving 833 citations
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 610
- Information Systems 305
- Computational Theory and Mathematics 161
- Molecular Biology 143
- Computer Vision and Pattern Recognition 76
Countries citing papers authored by Bojan Cestnik
This map shows the geographic impact of Bojan Cestnik'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 Bojan Cestnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bojan Cestnik more than expected).
Fields of papers citing papers by Bojan Cestnik
This network shows the impact of papers produced by Bojan Cestnik. 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 Bojan Cestnik. The network helps show where Bojan Cestnik may publish in the future.
Co-authorship network of co-authors of Bojan Cestnik
This figure shows the co-authorship network connecting the top 25 collaborators of Bojan Cestnik. A scholar is included among the top collaborators of Bojan Cestnik 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 Bojan Cestnik. Bojan Cestnik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 3 | |
| 3 | Bisociative Literature-Based Discovery: Lessons Learned and New Prospects. | 1 |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | 23 | |
| 8 | 2 | |
| 9 | Cross-domain literature mining: Finding bridging concepts with CrossBee. | 8 |
| 10 | 4 | |
| 11 | 39 | |
| 12 | Structuring Domain Knowledge by Semi-automatic Ontology Construction | 2 |
| 13 | Discovering Hidden Knowledge from Biomedical Literature | 13 |
| 14 | 50 | |
| 15 | A dataset decomposition approach to data mining and machine discovery | 5 |
| 16 | 18 | |
| 17 | Using the m -estimate in rule induction | 33 |
| 18 | Estimating probabilities: a crucial task in machine learning | 284 |
| 19 | Learning Redundant Rules in Noisy Domains. | 7 |
| 20 | ASSISTANT 86: a knowledge-elicitation tool for sophisticated users | 279 |
About Bojan Cestnik
Bojan Cestnik is a scholar working on Artificial Intelligence, Family Practice and Information Systems, having authored 37 papers that have together received 938 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (11 papers), Advanced Text Analysis Techniques (7 papers) and Topic Modeling (5 papers). The work is most often cited by research in Artificial Intelligence (610 citations), Information Systems (305 citations) and Health Information Management (47 citations). Bojan Cestnik has collaborated with scholars based in Slovenia, Norway and Croatia. Frequent co-authors include Ivan Bratko, Igor Kononenko, Nada Lavrač, Peter Flach, Dragan Gamberger, Tanja Urbančič, Marko Bohanec, Sašo Džeroski, Marko Debeljak and Andrej Kobler. Their work appears in journals such as Expert Systems with Applications, BMC Bioinformatics and Sustainability.
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.