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ORCIDTomás Kliegr
Tomáš Kliegr
Person information
- unicode name: Tomáš Kliegr
- affiliation: Prague University of Economics and Business, Department of Information and Knowledge Engineering, Prague, Czech Republic
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2020 – today
- 2025
[j20]Daniel Adam
, Tomás Kliegr:
Traceable LLM-based validation of statements in knowledge graphs. Inf. Process. Manag. 62(4): 104128 (2025)- [j19]Vojtech Balek, Lukás Sýkora, Vilém Sklenák, Tomás Kliegr:
LLM-based feature generation from text for interpretable machine learning. Mach. Learn. 114(11): 241 (2025) - [j18]Lucie Dvorackova, Marcin P. Joachimiak, Michal Cerny, Adriana Kubecova, Vilém Sklenák, Tomás Kliegr:
Explaining word embeddings with perfect fidelity: a case study in predicting research impact. Mach. Learn. 114(12): 265 (2025) - [j17]Lukás Sýkora, Tomás Kliegr:
action-rules: GPU-accelerated Python package for counterfactual explanations and recommendations. SoftwareX 29: 102000 (2025) - [e7]Alessandro Margara, Tomás Kliegr, Ognjen Savkovic, Shqiponja Ahmetaj, Riccardo Tommasini, Luigi Bellomarini, Evgeny Kharlamov, Ioana Georgiana Ciuciu, Dumitru Roman, George Konstantinidis, Emanuel Sallinger, Ahmet Soylu:
Companion Proceedings of the 9th International Joint Conference on Rules and Reasoning, RuleML+RR 2025, also co-located with 21th Reasoning Web Summer School, RW 2025, and 17th DecisionCAMP 2025 as part of Declarative AI 2025, Istanbul, Türkiye, September 22-24, 2025. CEUR Workshop Proceedings 4083, CEUR-WS.org 2025 [contents] - 2024
- [j16]Martin Atzmueller, Johannes Fürnkranz, Tomás Kliegr, Ute Schmid:
Explainable and interpretable machine learning and data mining. Data Min. Knowl. Discov. 38(5): 2571-2595 (2024) - [e6]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I. Communications in Computer and Information Science 1947, Springer 2024, ISBN 978-3-031-50395-5 [contents] - [e5]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II. Communications in Computer and Information Science 1948, Springer 2024, ISBN 978-3-031-50484-6 [contents] - [i10]Agnieszka Lawrynowicz, Luis Galárraga, Mehwish Alam, Berenice Jaulmes, Vaclav Zeman, Tomás Kliegr:
Neurosymbolic Methods for Rule Mining. CoRR abs/2408.05773 (2024) - [i9]Vojtech Balek, Lukás Sýkora, Vilém Sklenák, Tomás Kliegr:
LLM-based feature generation from text for interpretable machine learning. CoRR abs/2409.07132 (2024) - [i8]Daniel Adam, Tomás Kliegr:
Traceable LLM-based validation of statements in knowledge graphs. CoRR abs/2409.07507 (2024) - [i7]Lucie Dvorackova, Marcin P. Joachimiak, Michal Cerny, Adriana Kubecova, Vilém Sklenák, Tomás Kliegr:
Explaining word embeddings with perfect fidelity: Case study in research impact prediction. CoRR abs/2409.15912 (2024) - [i6]Milena Chadimová, Eduard Jurásek, Tomás Kliegr:
Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning. CoRR abs/2411.17304 (2024) - 2023
- [j15]Tomás Kliegr, Ebroul Izquierdo:
QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation. Appl. Intell. 53(18): 20797-20827 (2023) - [j14]Tomás Kliegr, Víctor Gutiérrez-Basulto, Ahmet Soylu:
Introduction to the Special Issue on Logic Rules and Reasoning: Selected Papers from the 4th International Joint Conference on Rules and Reasoning (RuleML+RR 2020). Theory Pract. Log. Program. 23(3): 503-506 (2023) - [c35]Lukás Sýkora, Tomás Kliegr:
Apriori Modified for Action Rules Mining. K-CAP 2023: 30-34 - [e4]Jan Vanthienen, Tomás Kliegr, Paul Fodor, Davide Lanti, Dörthe Arndt, Egor V. Kostylev, Theodoros Mitsikas, Ahmet Soylu:
Proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 co-located with 19th Reasoning Web Summer School (RW 2023) and 15th DecisionCAMP 2023 as part of Declarative AI 2023, Oslo, Norway, 18 - 20 September, 2023. CEUR Workshop Proceedings 3485, CEUR-WS.org 2023 [contents] - 2022
- [j13]Jirí Zárský, Gaetan Lopez, Tomás Kliegr:
Explainability of Text Clustering Visualizations - Twitter Disinformation Case Study. IEEE Computer Graphics and Applications 42(4): 8-19 (2022) - [j12]Lucie Beranová, Marcin P. Joachimiak, Tomás Kliegr, Gollam Rabby, Vilém Sklenák:
Why was this cited? Explainable machine learning applied to COVID-19 research literature. Scientometrics 127(5): 2313-2349 (2022) - [c34]Lukás Sýkora, Tomás Kliegr, Katerina Hrudková:
High-Utility Action Rules Mining. RuleML+RR (Companion) 2022 - 2021
- [j11]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artif. Intell. 295: 103458 (2021) - [j10]Václav Zeman, Tomás Kliegr, Vojtech Svátek:
RDFRules: Making RDF rule mining easier and even more efficient. Semantic Web 12(4): 569-602 (2021) - [e3]Martin Atzmüller, Tomás Kliegr, Ute Schmid:
Proceedings of the First International Workshop on Explainable and Interpretable Machine Learning (XI-ML 2020) co-located with the 43rd German Conference on Artificial Intelligence (KI 2020), Bamberg, Germany, September 21, 2020 (Virtual Workshop). CEUR Workshop Proceedings 2796, CEUR-WS.org 2021 [contents] - 2020
- [j9]Stanislav Vojír, Tomás Kliegr:
Editable machine learning models? A rule-based framework for user studies of explainability. Adv. Data Anal. Classif. 14(4): 785-799 (2020) - [j8]Johannes Fürnkranz, Tomás Kliegr, Heiko Paulheim:
On cognitive preferences and the plausibility of rule-based models. Mach. Learn. 109(4): 853-898 (2020) - [c33]Lukás Sýkora, Tomás Kliegr:
Action Rules: Counterfactual Explanations in Python. RuleML+RR (Supplement) 2020: 28-41 - [e2]Víctor Gutiérrez-Basulto, Tomás Kliegr, Ahmet Soylu, Martin Giese, Dumitru Roman:
Rules and Reasoning - 4th International Joint Conference, RuleML+RR 2020, Oslo, Norway, June 29 - July 1, 2020, Proceedings. Lecture Notes in Computer Science 12173, Springer 2020, ISBN 978-3-030-57976-0 [contents]
2010 – 2019
- 2019
- [j7]Michael Hahsler, Ian Johnson, Tomás Kliegr, Jaroslav Kuchar:
Associative Classification in R: arc, arulesCBA, and rCBA. R J. 11(2): 254 (2019) - [c32]Tomás Kliegr, Jaroslav Kuchar:
Tuning Hyperparameters of Classification Based on Associations (CBA). ITAT 2019: 9-16 - [c31]Jirí Filip, Tomás Kliegr:
PyIDS - Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. RuleML+RR (Supplement) 2019 - [i5]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
Advances in Machine Learning for the Behavioral Sciences. CoRR abs/1911.03249 (2019) - 2018
- [j6]Tomás Kliegr, Ondrej Zamazal:
Antonyms are similar: Towards paradigmatic association approach to rating similarity in SimLex-999 and WordSim-353. Data Knowl. Eng. 115: 174-193 (2018) - [j5]Stanislav Vojír, Vaclav Zeman, Jaroslav Kuchar, Tomás Kliegr:
EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets. Knowl. Based Syst. 150: 111-115 (2018) - [c30]Johannes Fürnkranz, Tomás Kliegr:
The Need for Interpretability Biases. IDA 2018: 15-27 - [c29]Jirí Filip, Tomás Kliegr:
Classification based on Associations (CBA) - A Performance Analysis. RuleML+RR (Supplement) 2018 - [c28]Vaclav Zeman, Tomás Kliegr, Vojtech Svátek:
RdfRules Preview: Towards an Analytics Engine for Rule Mining in RDF Knowledge Graphs. RuleML+RR (Supplement) 2018 - [i4]Johannes Fürnkranz, Tomás Kliegr, Heiko Paulheim:
On Cognitive Preferences and the Interpretability of Rule-based Models. CoRR abs/1803.01316 (2018) - [i3]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. CoRR abs/1804.02969 (2018) - 2017
- [b1]Tomás Kliegr:
Effect of cognitive biases on human understanding of rule-based machine learning models. Queen Mary University of London, UK, 2017 - [j4]Jaroslav Kuchar, Tomás Kliegr:
InBeat: JavaScript recommender system supporting sensor input and linked data. Knowl. Based Syst. 135: 40-43 (2017) - [c27]Tomás Kliegr, Jaroslav Kuchar, Stanislav Vojír, Václav Zeman:
EasyMiner - Short History of Research and Current Development. ITAT 2017: 235-239 - [c26]Jaroslav Kuchar, Adam Ashenfelter, Tomás Kliegr:
Outlier (Anomaly) Detection Modelling in PMML. RuleML+RR (Supplement) 2017 - [c25]Stanislav Vojír, Vaclav Zeman, Jaroslav Kuchar, Tomás Kliegr:
Using EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project. RuleML+RR (Supplement) 2017 - [i2]Tomás Kliegr:
Quantitative CBA: Small and Comprehensible Association Rule Classification Models. CoRR abs/1711.10166 (2017) - 2016
- [j3]Tomás Kliegr, Ondrej Zamazal:
LHD 2.0: A text mining approach to typing entities in knowledge graphs. J. Web Semant. 39: 47-61 (2016) - [c24]Milan Dojchinovski, Dinesh Reddy, Tomás Kliegr, Tomas Vitvar, Harald Sack:
Crowdsourced Corpus with Entity Salience Annotations. LREC 2016 - 2015
- [j2]Tomás Kliegr:
Linked hypernyms: Enriching DBpedia with Targeted Hypernym Discovery. J. Web Semant. 31: 59-69 (2015) - [c23]Tomás Kliegr, Jaroslav Kuchar:
Benchmark of Rule-Based Classifiers in the News Recommendation Task. CLEF 2015: 130-141 - [c22]Johannes Fürnkranz, Tomás Kliegr:
A Brief Overview of Rule Learning. RuleML 2015: 54-69 - [c21]Stanislav Vojír, Vaclav Zeman, Jaroslav Kuchar, Tomás Kliegr:
EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R. Challenge+DC@RuleML 2015 - [p1]Damianos Galanopoulos, Milan Dojchinovski, Krishna Chandramouli, Tomás Kliegr, Vasileios Mezaris:
Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video. Multimedia Data Mining and Analytics 2015: 295-310 - [e1]Nick Bassiliades, Paul Fodor, Adrian Giurca, Georg Gottlob, Tomás Kliegr, Grzegorz J. Nalepa, Monica Palmirani, Adrian Paschke, Mark Proctor, Dumitru Roman, Fariba Sadri, Nenad Stojanovic:
Proceedings of the RuleML 2015 Challenge, the Special Track on Rule-based Recommender Systems for the Web of Data, the Special Industry Track and the RuleML 2015 Doctoral Consortium hosted by the 9th International Web Rule Symposium (RuleML 2015), Berlin, Germany, August 2-5, 2015. CEUR Workshop Proceedings 1417, CEUR-WS.org 2015 [contents] - 2014
- [c20]Jaroslav Kuchar, Tomás Kliegr:
InBeat: Recommender System as a Service. CLEF (Working Notes) 2014: 837-844 - [c19]Tomás Kliegr, Jaroslav Kuchar:
Orwellian Eye: Video Recommendation with Microsoft Kinect. ECAI 2014: 1227-1228 - [c18]Tomás Kliegr, Ondrej Zamazal:
Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery. LREC 2014: 3517-3523 - [c17]Tomás Kliegr, Jaroslav Kuchar, Davide Sottara, Stanislav Vojír:
Learning Business Rules with Association Rule Classifiers. RuleML 2014: 236-250 - [c16]Stanislav Vojír, Premysl Václav Duben, Tomás Kliegr:
Business Rule Learning with Interactive Selection of Association Rules. Challenge+DC@RuleML 2014 - 2013
- [c15]Julien Leroy, François Rocca, Matei Mancas, Radhwan Ben Madhkour, Fabien Grisard, Tomás Kliegr, Jaroslav Kuchar, Jakub Vit, Ivan Pirner, Petr Zimmermann:
KINterestTV - Towards Non-invasive Measure of User Interest While Watching TV. eNTERFACE 2013: 179-199 - [c14]Milan Dojchinovski, Tomás Kliegr:
Entityclassifier.eu: Real-Time Classification of Entities in Text with Wikipedia. ECML/PKDD (3) 2013: 654-658 - [c13]Jaroslav Kuchar, Tomás Kliegr:
GAIN: web service for user tracking and preference learning - a smart TV use case. RecSys 2013: 467-468 - [c12]Stanislav Vojír, Tomás Kliegr, Andrej Hazucha, Radek Skrabal, Milan Simunek:
Transforming Association Rules to Business Rules: EasyMiner meets Drools. RuleML (2) 2013 - [c11]Milan Dojchinovski, Tomás Kliegr:
Datasets, GATE Evaluation Framework for Benchmarking Wikipedia-Based NER Systems. NLP-DBPEDIA@ISWC 2013 - [i1]Milan Dojchinovski, Ivo Lasek, Tomás Kliegr, Ondrej Zamazal:
Wikipedia Search as Effective Entity Linking Algorithm. TAC 2013 - 2012
- [c10]Radek Skrabal, Milan Simunek, Stanislav Vojír, Andrej Hazucha, Tomás Marek, David Chudán, Tomás Kliegr:
Association Rule Mining Following the Web Search Paradigm. ECML/PKDD (2) 2012: 808-811 - [c9]Dorothea Tsatsou, Lyndon J. B. Nixon, Matei Mancas, Miroslav Vacura, Rüdiger Klein, Julien Leroy, Jaroslav Kuchar, Tomás Kliegr, Manuel Kober, Maria Loli, Vasileios Mezaris:
Contextualised user profiling in networked media environments. UMAP Workshops 2012 - 2011
- [j1]Tomás Kliegr, Vojtech Svátek, Martin Ralbovský, Milan Simunek:
SEWEBAR-CMS: semantic analytical report authoring for data mining results. J. Intell. Inf. Syst. 37(3): 371-395 (2011) - [c8]Tomás Kliegr, Andrej Hazucha, Tomás Marek:
Instant Feedback on Discovered Association Rules with PMML-Based Query-by-Example. RR 2011: 257-262 - [c7]Stanislav Vojír, Tomás Kliegr, Vojtech Svátek, Ondrej Sváb-Zamazal:
Automated matching of data mining dataset schemata to background knowledge. OM 2011 - 2010
- [c6]Tomás Kliegr:
Entity classification by bag of Wikipedia articles. PIKM 2010: 67-74 - [c5]Tomás Kliegr, David Chudán, Andrej Hazucha, Jan Rauch:
SEWEBAR-CMS: A System for Postprocessing Data Mining Models. RuleML Challenge 2010 - [c4]Tomás Kliegr, Jan Rauch:
An XML Format for Association Rule Models Based on the GUHA Method. RuleML 2010: 273-288
2000 – 2009
- 2009
- [c3]Krishna Chandramouli, Tomás Kliegr, Vojtech Svátek, Ebroul Izquierdo:
Towards semantic tagging in collaborative environments. DPS 2009: 1-6 - [c2]Tomás Kliegr, Martin Ralbovský, Vojtech Svátek, Milan Simunek, Vojtech Jirkovský, Jan Nemrava, Jan Zemánek:
Semantic Analytical Reports: A Framework for Post-processing Data Mining Results. ISMIS 2009: 88-98 - 2008
- [c1]Jan Nemrava, Tomás Kliegr, Vojtech Svátek, Martin Ralbovský, Jirí Splíchal, Tomás Vejlupek:
Semantic annotation and linking of competitive intelligence reports for business clusters. OBI 2008: 9
Coauthor Index
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last updated on 2025-12-09 00:38 CET by the dblp team
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