| Query refinement and user relevance feedback for contextualized image retrieval K Chandramouli, T Kliegr, J Nemrava, V Svátek, E Izquierdo IET Digital Library, 2008 | 50 | 2008 |
| A brief overview of rule learning J Fürnkranz, T Kliegr International symposium on rules and rule markup languages for the semantic …, 2015 | 48 | 2015 |
| Entityclassifier. eu: real-time classification of entities in text with Wikipedia M Dojchinovski, T Kliegr Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 44 | 2013 |
| Combining image captions and visual analysis for image concept classification T Kliegr, K Chandramouli, J Nemrava, V Svatek, E Izquierdo Proceedings of the 9th International Workshop on Multimedia Data Mining …, 2008 | 43 | 2008 |
| Linked hypernyms: Enriching dbpedia with targeted hypernym discovery T Kliegr Journal of Web Semantics 31, 59-69, 2015 | 34 | 2015 |
| On cognitive preferences and the plausibility of rule-based models J Fürnkranz, T Kliegr, H Paulheim Machine Learning 109 (4), 853-898, 2020 | 30* | 2020 |
| Learning business rules with association rule classifiers T Kliegr, J Kuchař, D Sottara, S Vojíř International Symposium on Rules and Rule Markup Languages for the Semantic …, 2014 | 29 | 2014 |
| LHD 2.0: A text mining approach to typing entities in knowledge graphs T Kliegr, O Zamazal Journal of Web Semantics 39, 47-61, 2016 | 25 | 2016 |
| Semantic analytical reports: A framework for post-processing data mining results T Kliegr, M Ralbovský, V Svátek, M Šimůnek, V Jirkovský, J Nemrava, ... International Symposium on Methodologies for Intelligent Systems, 88-98, 2009 | 22 | 2009 |
| A review of possible effects of cognitive biases on interpretation of rule-based machine learning models T Kliegr, Š Bahník, J Fürnkranz Artificial Intelligence, 103458, 2021 | 18 | 2021 |
| EasyMiner. eu: Web framework for interpretable machine learning based on rules and frequent itemsets S Vojíř, V Zeman, J Kuchař, T Kliegr Knowledge-Based Systems 150, 111-115, 2018 | 16 | 2018 |
| UTA-NM: Explaining stated preferences with additive non-monotonic utility functions T Kliegr Preference Learning, 56, 2009 | 15 | 2009 |
| Benchmark of rule-based classifiers in the news recommendation task T Kliegr, J Kuchař International Conference of the Cross-Language Evaluation Forum for European …, 2015 | 13 | 2015 |
| Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery and statistical type inferrence T Kliegr, O Zamazal Proceedings of The Ninth International Conference on Language Resources and …, 2014 | 12 | 2014 |
| SEWEBAR-CMS: semantic analytical report authoring for data mining results T Kliegr, V Svátek, M Ralbovský, M Šimůnek Journal of Intelligent Information Systems 37 (3), 371-395, 2011 | 12 | 2011 |
| Entity classification by bag of Wikipedia articles T Kliegr Proceedings of the 3rd workshop on Ph. D. students in information and …, 2010 | 12 | 2010 |
| An XML format for association rule models based on the GUHA method T Kliegr, J Rauch International Workshop on Rules and Rule Markup Languages for the Semantic …, 2010 | 12 | 2010 |
| Association rule mining following the web search paradigm R Škrabal, M Šimůnek, S Vojíř, A Hazucha, T Marek, D Chudán, T Kliegr Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 11 | 2012 |
| Crowdsourced corpus with entity salience annotations M Dojchinovski, D Reddy, T Kliegr, T Vitvar, H Sack Proceedings of the Tenth International Conference on Language Resources and …, 2016 | 10 | 2016 |
| Transforming association rules to business rules: EasyMiner meets Drools S Vojır, T Kliegr, A Hazucha, R Škrabal, M Šimunek RuleML-2013 Challenge. CEUR-WS. org 49, 2013 | 10 | 2013 |
