Semantic annotation and linking of competitive intelligence reports for business clusters. - Jan Nemrava; Tomás Kliegr; Vojtech Svátek; Martin Ralbovský; Jirí Splíchal; Tomás Vejlupek (OBI, 2008)

Towards semantic tagging in collaborative environments. - Krishna Chandramouli; Tomás Kliegr; Vojtech Svátek; Ebroul Izquierdo (DPS, 2009)

Semantic Analytical Reports: A Framework for Post-processing Data Mining Results. - Tomás Kliegr; Martin Ralbovský; Vojtech Svátek; Milan Simunek; Vojtech Jirkovský; Jan Nemrava; Jan Zemánek (ISMIS, 2009)

Entity classification by bag of Wikipedia articles. - Tomás Kliegr (PIKM, 2010)

SEWEBAR-CMS: A System for Postprocessing Data Mining Models. - Tomás Kliegr; David Chudán; Andrej Hazucha; Jan Rauch (RuleML Challenge, 2010)

An XML Format for Association Rule Models Based on the GUHA Method. - Tomás Kliegr; Jan Rauch (RuleML, 2010)

Instant Feedback on Discovered Association Rules with PMML-Based Query-by-Example. - Tomás Kliegr; Andrej Hazucha; Tomás Marek (RR, 2011)

Automated matching of data mining dataset schemata to background knowledge. - Stanislav Vojír; Tomás Kliegr; Vojtech Svátek; Ondrej Sváb-Zamazal (OM, 2011)

SEWEBAR-CMS: semantic analytical report authoring for data mining results. - Tomás Kliegr; Vojtech Svátek; Martin Ralbovský; Milan Simunek (J. Intell. Inf. Syst., 2011)

Association Rule Mining Following the Web Search Paradigm. - Radek Skrabal; Milan Simunek; Stanislav Vojír; Andrej Hazucha; Tomás Marek; David Chudán; Tomás Kliegr (ECML/PKDD (2), 2012)

Contextualised user profiling in networked media environments. - Dorothea Tsatsou; Lyndon J. B. Nixon; Matei Mancas; Miroslav Vacura; Rüdiger Klein; Julien Leroy 001; Jaroslav Kuchar; Tomás Kliegr; Manuel Kober; Maria Loli; Vasileios Mezaris (UMAP Workshops, 2012)

KINterestTV - Towards Non-invasive Measure of User Interest While Watching TV. - Julien Leroy 001; François Rocca; Matei Mancas; Radhwan Ben Madhkour; Fabien Grisard; Tomás Kliegr; Jaroslav Kuchar; Jakub Vit; Ivan Pirner; Petr Zimmermann (eNTERFACE, 2013)

Entityclassifier.eu: Real-Time Classification of Entities in Text with Wikipedia. - Milan Dojchinovski; Tomás Kliegr (ECML/PKDD (3), 2013)

GAIN: web service for user tracking and preference learning - a smart TV use case. - Jaroslav Kuchar; Tomás Kliegr (RecSys, 2013)

Transforming Association Rules to Business Rules: EasyMiner meets Drools. - Stanislav Vojír; Tomás Kliegr; Andrej Hazucha; Radek Skrabal; Milan Simunek (RuleML (2), 2013)

Datasets, GATE Evaluation Framework for Benchmarking Wikipedia-Based NER Systems. - Milan Dojchinovski; Tomás Kliegr (NLP-DBPEDIA@ISWC, 2013)

Wikipedia Search as Effective Entity Linking Algorithm. - Milan Dojchinovski; Ivo Lasek; Tomás Kliegr; Ondrej Zamazal (TAC, 2013)

InBeat: Recommender System as a Service. - Jaroslav Kuchar; Tomás Kliegr (CLEF (Working Notes), 2014)

Orwellian Eye: Video Recommendation with Microsoft Kinect. - Tomás Kliegr; Jaroslav Kuchar (ECAI, 2014)

Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery. - Tomás Kliegr; Ondrej Zamazal (LREC, 2014)

Learning Business Rules with Association Rule Classifiers. - Tomás Kliegr; Jaroslav Kuchar; Davide Sottara; Stanislav Vojír (RuleML, 2014)

Business Rule Learning with Interactive Selection of Association Rules. - Stanislav Vojír; Premysl Václav Duben; Tomás Kliegr (Challenge+DC@RuleML, 2014)

Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video. - Damianos Galanopoulos; Milan Dojchinovski; Krishna Chandramouli; Tomás Kliegr; Vasileios Mezaris (Multimedia Data Mining and Analytics, 2015)

Benchmark of Rule-Based Classifiers in the News Recommendation Task. - Tomás Kliegr; Jaroslav Kuchar (CLEF, 2015)

A Brief Overview of Rule Learning. - Johannes Fürnkranz; Tomás Kliegr (RuleML, 2015)

EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (Challenge+DC@RuleML, 2015)

Linked hypernyms: Enriching DBpedia with Targeted Hypernym Discovery. - Tomás Kliegr (J. Web Semant., 2015)

Crowdsourced Corpus with Entity Salience Annotations. - Milan Dojchinovski; Dinesh Reddy; Tomás Kliegr; Tomas Vitvar; Harald Sack (LREC, 2016)

LHD 2.0: A text mining approach to typing entities in knowledge graphs. - Tomás Kliegr; Ondrej Zamazal (J. Web Semant., 2016)

EasyMiner - Short History of Research and Current Development. - Tomás Kliegr; Jaroslav Kuchar; Stanislav Vojír; Václav Zeman (ITAT, 2017)

Outlier (Anomaly) Detection Modelling in PMML. - Jaroslav Kuchar; Adam Ashenfelter; Tomás Kliegr (RuleML+RR (Supplement), 2017)

Using EasyMiner API for Financial Data Analysis in the OpenBudgets.eu Project. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (RuleML+RR (Supplement), 2017)

Quantitative CBA: Small and Comprehensible Association Rule Classification Models. - Tomás Kliegr (CoRR, 2017)

InBeat: JavaScript recommender system supporting sensor input and linked data. - Jaroslav Kuchar; Tomás Kliegr (Knowl. Based Syst., 2017)

Effect of cognitive biases on human understanding of rule-based machine learning models. - Tomás Kliegr (2017)

The Need for Interpretability Biases. - Johannes Fürnkranz; Tomás Kliegr (IDA, 2018)

Classification based on Associations (CBA) - A Performance Analysis. - Jirí Filip; Tomás Kliegr (RuleML+RR (Supplement), 2018)

RdfRules Preview: Towards an Analytics Engine for Rule Mining in RDF Knowledge Graphs. - Vaclav Zeman; Tomás Kliegr; Vojtech Svátek (RuleML+RR (Supplement), 2018)

On Cognitive Preferences and the Interpretability of Rule-based Models. - Johannes Fürnkranz; Tomás Kliegr; Heiko Paulheim (CoRR, 2018)

Antonyms are similar: Towards paradigmatic association approach to rating similarity in SimLex-999 and WordSim-353. - Tomás Kliegr; Ondrej Zamazal (Data Knowl. Eng., 2018)

EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets. - Stanislav Vojír; Vaclav Zeman; Jaroslav Kuchar; Tomás Kliegr (Knowl. Based Syst., 2018)

Tuning Hyperparameters of Classification Based on Associations (CBA). - Tomás Kliegr; Jaroslav Kuchar (ITAT, 2019)

PyIDS - Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. - Jirí Filip; Tomás Kliegr (RuleML+RR (Supplement), 2019)

Advances in Machine Learning for the Behavioral Sciences. - Tomás Kliegr; Stepán Bahník; Johannes Fürnkranz (CoRR, 2019)

Associative Classification in R: arc, arulesCBA, and rCBA. - Michael Hahsler; Ian Johnson; Tomás Kliegr; Jaroslav Kuchar (R J., 2019)

Action Rules: Counterfactual Explanations in Python. - Lukás Sýkora; Tomás Kliegr (RuleML+RR (Supplement), 2020)

Editable machine learning models? A rule-based framework for user studies of explainability. - Stanislav Vojír; Tomás Kliegr (Adv. Data Anal. Classif., 2020)

On cognitive preferences and the plausibility of rule-based models. - Johannes Fürnkranz; Tomás Kliegr; Heiko Paulheim (Mach. Learn., 2020)

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. - Tomás Kliegr; Stepán Bahník; Johannes Fürnkranz (Artif. Intell., 2021)

RDFRules: Making RDF rule mining easier and even more efficient. - Václav Zeman; Tomás Kliegr; Vojtech Svátek (Semantic Web, 2021)

High-Utility Action Rules Mining. - Lukás Sýkora; Tomás Kliegr; Katerina Hrudková (RuleML+RR (Companion), 2022)

Explainability of Text Clustering Visualizations - Twitter Disinformation Case Study. - Jirí Zárský; Gaetan Lopez; Tomás Kliegr (IEEE Computer Graphics and Applications, 2022)

Why was this cited? Explainable machine learning applied to COVID-19 research literature. - Lucie Beranová; Marcin P. Joachimiak; Tomás Kliegr; Gollam Rabby; Vilém Sklenák (Scientometrics, 2022)

Apriori Modified for Action Rules Mining. - Lukás Sýkora; Tomás Kliegr (K-CAP, 2023)

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). - Tomás Kliegr; Víctor Gutiérrez-Basulto; Ahmet Soylu (Theory Pract. Log. Program., 2023)

QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation. - Tomás Kliegr; Ebroul Izquierdo (Appl. Intell., 2023)

Neurosymbolic Methods for Rule Mining. - Agnieszka Lawrynowicz; Luis Galárraga; Mehwish Alam; Berenice Jaulmes; Vaclav Zeman; Tomás Kliegr (CoRR, 2024)

LLM-based feature generation from text for interpretable machine learning. - Vojtech Balek; Lukás Sýkora; Vilém Sklenák; Tomás Kliegr (CoRR, 2024)

Explaining word embeddings with perfect fidelity: Case study in research impact prediction. - Lucie Dvorackova; Marcin P. Joachimiak; Michal Cerny; Adriana Kubecova; Vilém Sklenák; Tomás Kliegr (CoRR, 2024)

Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning. - Milena Chadimová; Eduard Jurásek; Tomás Kliegr (CoRR, 2024)

Explainable and interpretable machine learning and data mining. - Martin Atzmueller; Johannes Fürnkranz; Tomás Kliegr; Ute Schmid (Data Min. Knowl. Discov., 2024)

Traceable LLM-based validation of statements in knowledge graphs. - Daniel Adam; Tomás Kliegr (Inf. Process. Manag., 2025)

action-rules: GPU-accelerated Python package for counterfactual explanations and recommendations. - Lukás Sýkora; Tomás Kliegr (SoftwareX, 2025)