Jönköping AI Lab – JAIL

Article (non-refereed)

• Green, D., Lavesson, N. (2019). Chaos theory and artificial intelligence may provide insights on disability outcomes. Developmental Medicine & Child Neurology 61(10), 1120-1120

Article (refereed)

• Gleicher, M., Riveiro, M., Von Landesberger, T., Deussen, O., Chang, R., Gillman, C. (2023). A Problem Space for Designing Visualizations. IEEE Computer Graphics and Applications 43(4), 111-120
• Johansson, U., Löfström, T., Boström, H. (2023). Conformal Predictive Distribution Trees. Annals of Mathematics and Artificial Intelligence
• Löfström, H., Löfström, T., Johansson, U., Sönströd, C. (2023). Investigating the impact of calibration on the quality of explanations. Annals of Mathematics and Artificial Intelligence
• Pomarlan, M., Hedblom, M., Porzel, R. (2023). Curiously exploring affordance spaces of a pouring task. Expert systems (Print) 40(6),
• Shimizu, C., Hammar, K., Hitzler, P. (2023). Modular ontology modeling. Semantic Web 14(3), 459-489
• Höffner, S., Porzel, R., Hedblom, M., Pomarlan, M., Cangalovic, V., Pfau, J. ... Malaka, R. (2022). Deep understanding of everyday activity commands for household robots. Semantic Web 13(5), 895-909
• Johansson, U., Sönströd, C., Löfström, T., Boström, H. (2022). Rule extraction with guarantees from regression models. Pattern Recognition
• Kebede, R., Moscati, A., Tan, H., Johansson, P. (2022). Integration of manufacturers' product data in BIM platforms using semantic web technologies. Automation in Construction
• Arvidsson, S., Gullstrand, M., Sirmacek, B., Riveiro, M. (2021). Sensor fusion and convolutional neural networks for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data. Sensors 21(4), 1-21
• Beauxis-Aussalet, E., Behrisch, M., Borgo, R., Chau, D., Collins, C., Ebert, D. ... Rhyne, T. (2021). The Role of Interactive Visualization in Fostering Trust in AI. IEEE Computer Graphics and Applications 41(6), 7-12
• García-Martín, E., Bifet, A., Lavesson, N. (2021). Energy modeling of Hoeffding tree ensembles. Intelligent Data Analysis 25(1), 81-104
• García-Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2021). Energy-aware very fast decision tree. International Journal of Data Science and Analytics 11(2), 105-126
• Kusetogullari, H., Yavariabdi, A., Hall, J., Lavesson, N. (2021). DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset. Big Data Research
• Oucheikh, R., Löfström, T., Ahlberg, E., Carlsson, L. (2021). Rolling cargo management using a deep reinforcement learning approach. Logistics 5(1),
• Oucheikh, R., Pettersson, T., Löfström, T. (2021). Product verification using OCR classification and Mondrian conformal prediction. Expert systems with applications
• Ouchiekh, R., Fri, M., Touil, A., Echchatbi, A. (2021). Total weighted tardiness in the permutation flow shop under uncertainty. IFAC-PapersOnLine series 54(1), 1174-1180
• Riveiro, M., Thill, S. (2021). “That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems. Artificial Intelligence
• Tan, H., Tarasov, V., Jarfors, A., Seifeddine, S. (2021). A design of fuzzy inference systems to predict tensile properties of as-cast alloy. The International Journal of Advanced Manufacturing Technology 113(3-4), 1111-1123
• Ulfenborg, B., Karlsson, A., Riveiro, M., Andersson, C., Sartipy, P., Synnergren, J. (2021). Multi-assignment clustering: Machine learning from a biological perspective. Journal of Biotechnology (pp. 1-10).
• Ventocilla, E., Martins, R., Paulovich, F., Riveiro, M. (2021). Scaling the Growing Neural Gas for Visual Cluster Analysis. Big Data Research
• Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M., Falkman, G. (2020). Interactive Clustering: A Comprehensive Review. ACM Digital Library
• Linusson, H., Johansson, U., Boström, H. (2020). Efficient conformal predictor ensembles. Neurocomputing (pp. 266-278).
• Riveiro, M. (2020). Explainable AI for maritime anomaly detection and autonomous driving. Dagstuhl Reports 9(11), 29-30
• Sirmacek, B., Riveiro, M. (2020). Occupancy prediction using low-cost and low-resolution heat sensors for smart offices. Sensors 20(19),
• Tarasov, V., Tan, H., Jarfors, A., Seifeddine, S. (2020). Fuzzy logic-based modelling of yield strength of as-cast A356 alloy. Neural Computing & Applications 32(10), 5833-5844
• Ventocilla, E., Riveiro, M. (2020). A comparative user study of visualization techniques for cluster analysis of multidimensional data sets. Information Visualization 19(4), 318-338
• Buendia, R., Kogej, T., Engkvist, O., Carlsson, L., Linusson, H., Johansson, U. ... Ahlberg, E. (2019). Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. Journal of Chemical Information and Modeling 59(3), 1230-1237
• Dórea, F., Vial, F., Hammar, K., Lindberg, A., Lambrix, P., Blomqvist, E., Revie, C. (2019). Drivers for the development of an Animal Health Surveillance Ontology (AHSO). Preventive Veterinary Medicine (pp. 39-48).
• Johansson, U., Löfström, T., Linusson, H., Boström, H. (2019). Efficient Venn Predictors using Random Forests. Machine Learning 108(3), 535-550
• Tan, H., Tarasov, V., Jarfors, A., Seifeddine, S. (2019). Fuzzy Logic Based Modelling of Cast Component Properties. IFAC-PapersOnLine 52(13), 1132-1137
• Alirezaie, M., Hammar, K., Blomqvist, E. (2018). SmartEnv as a Network of Ontology Patterns. Semantic Web 9(6), 903-918
• Johansson, U., Linusson, H., Löfström, T., Boström, H. (2018). Interpretable regression trees using conformal prediction. Expert systems with applications (pp. 394-404).
• Löfström, H., Löfström, T., Johansson, U. (2018). Interpretable instance-based text classification for social science research projects. Archives of Data Science, Series A 5(1),
• Westphal, F., Grahn, H., Lavesson, N. (2018). Efficient document image binarization using heterogeneous computing and parameter tuning. International Journal on Document Analysis and Recognition 21(1-2), 41-58
• Boström, H., Linusson, H., Löfström, T., Johansson, U. (2017). Accelerating difficulty estimation for conformal regression forests. Annals of Mathematics and Artificial Intelligence 81(1-2), 125-144
• Sandkuhl, K., Lin, F., Shilov, N., Smirnov, A., Tarasov, V., Krizhanovsky, A. (2013). Logistics-as-a-service: Ontology-based architecture and approach. Revista Investigación Operacional 34(3), 188-194
• Tarasov, V. (2012). Ontology-based Approach to Competence Profile Management. Journal of universal computer science (Online) 18(20), 2893-2919
• Tarasov, V., Lundqvist, M. (2007). Modeling Collaborative Design Competence with Ontologies. International Journal of e-collaboration 3(4), 46-62

Chapter in book

• Li, Z., Tan, H., Lattanzi, L., Jarfors, A., Jansson, P. (2023). On the Possibility of Replacing Scheil-Gulliver Modeling with Machine Learning and Neural Network Models. In: A. Pola, M. Tocci and A. Rassili (Ed.), Solid State Phenomena Trans Tech Publications
• Hedblom, M. (2022). Methodological reflection of the documentalysis of .co.kr from the %WRONG Browser series. In: I. Hinterwaldner, D. Hönigsberg & M. Konstantin (Ed.), Begriffe des digitalen Bildes Hildesheim: Open Publishing LMU
• Blomqvist, E., Hahmann, T., Hammar, K., Hitzler, P., Hoekstra, R., Mutharaju, R. ... Zhou, L. (2021). Preface. In: E. Blomqvist, T. Hahmann, K. Hammar, P. Hitzler, R. Hoekstra, R. Mutharaju, M. Poveda-Villalón, C. Shimizu, M. G. Skjæveland, M. Solanki, V. Svátek, & L. Zhou (Ed.), Studies on the Semantic Web Amsterdam: IOS Press
• Hammar, K. (2021). Ontology Design Principles for Model-Driven Applications. In: E. Blomqvist, T. Hahmann, K. Hammar, P. Hitzler, R. Hoekstra, R. Mutharaju, M. Poveda-Villalón, C. Shimizu, M. G. Skjæveland, M. Solanki, V. Svátek, & L. Zhou (Ed.), Studies on the Semantic Web Amsterdam: IOS Press
• Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P., Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
• Hitzler, P., Fernandez, M., Janowicz, K., Zaveri, A., Gray, A., Lopez, V. ... Hammar, K. (2019). Preface. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Springer
• Janowicz, K., Krisnadhi, A., Poveda Villalón, M., Hammar, K., Shimizu, C. (2019). Preface. In: CEUR Workshop Proceedings CEUR-WS
• García Martín, E., Lavesson, N., Grahn, H. (2017). Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm. In: Rokia Missaoui, Talel Abdessalem, Matthieu Latapy (Ed.), Lectures Notes in Social Networks Cham, Switzerland: Springer
• Tarasov, V., Tan, H., Ismail, M., Adlemo, A., Johansson, M. (2017). Application of inference rules to a software requirements ontology to generate software test cases. In: Lecture Notes in Computer Science Cham: Springer
• Blomqvist, E., Hammar, K., Presutti, V. (2016). Engineering Ontologies with Patterns - The eXtreme Design Methodology. In: Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti (Ed.), Studies on the Semantic Web IOS Press
• Hammar, K. (2016). Quality of Content Ontology Design Patterns. In: Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti (Ed.), Studies on the Semantic Web IOS Press
• Hammar, K., Blomqvist, E., Carral, D., Van Erp, M., Fokkens, A., Gangemi, A. ... Svatek, V. (2016). Collected Research Questions Concerning Ontology Design Patterns. In: Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti (Ed.), Studies on the Semantic Web IOS Press
• Tarasov, V., Sandkuhl, K., Lundqvist, M. (2010). Modeling Collaborative Design Competence with Ontologies. In: In Lee (Ed.), Encyclopedia of E-Business Development and Management in the Global Economy (pp. 942-951). IGI Global
• Tarasov, V., Sandkuhl, K., Lundqvist, M. (2009). Ontology-based Competence Model for Collaborative Design. In: Ned Kock (Ed.), Advances in E-Collaboration (AECOB) Book Series Hershey: Information Science Reference
• Albertsen, T., Sandkuhl, K., Tarasov, V. (2008). Towards Competence Modeling and Competence Matching for Network-Based Defense. In: M. Norsell (Ed.), Stockholm contributions in military-technology Stockholm: Swedish National Defence College
• Johansson, Ö., Tarasov, V. (2004). CaseMaster – an interactive tool for case-based learning over the network. In: F. Buchberger & K. Enser (Ed.), @ - Learning in Higher Education II (pp. 66-80). Linz: Trauner Verlag
• Tarasov, V., Paulsson, F. (2004). Development of a standard for representation of cases. In: F. Buchberger & K. Enser (Ed.), Schriften der pädagogischen Akademie des Bundes in Oberösterreich Linz: Trauner Verlag
• Tarasov, V., Johansson, B. (2002). Active learning models and instructional systems. In: F. Buchberger, S. Berghammer & K. Enser (Ed.), @ – Learning in Higher Education Linz: Trauner Verlag

Collection (editor)

• Blomqvist, E., Hahmann, T., Hammar, K., Hitzler, P., Hoekstra, R., Mutharaju, R. ... Zhou, L. (2021). Advances in Pattern-Based Ontology Engineering. Amsterdam: IOS Press

Conference paper

• Hedblom, M. (2023). When Push Comes to Shove: A Formal Analysis of the Conceptual Primitives in Pushing. CEUR-WS
• Johansson, P., Fischl, G., Hammar, K. (2023). Occupancy assessment for lighting evaluation using Digital Twin technology. Abingdon: CRC Press
• Johansson, U., Löfström, T., Sönströd, C., Löfström, H. (2023). Conformal Prediction for Accuracy Guarantees in Classification with Reject Option. Springer
• Johansson, U., Sönströd, C., Löfström, T., Boström, H. (2023). Confidence Classifiers with Guaranteed Accuracy or Precision. Proceedings of Machine Learning Research (PMLR)
• Kebede, R., Moscati, A., Tan, H., Johansson, P. (2023). Circular economy in the built environment: a framework for implementing digital product passports with knowledge graphs. European Council on Computing in Construction
• Li, Z., Tan, H., Jarfors, A., Lattanzi, L., Jansson, P. (2023). Enhancing Rheocasting Process Control with AI-based Systems.
• Löfström, T., Bondaletov, A., Ryasik, A., Boström, H., Johansson, U. (2023). Tutorial on using Conformal Predictive Systems in KNIME. Proceedings of Machine Learning Research (PMLR)
• Mossakowski, T., Hedblom, M., Neuhaus, F., Arboleda, S., Raake, A. (2023). Using the Diagrammatic Image Schema Language for Joint Human-Machine Cognition.
• Pettersson, T., Riveiro, M., Löfström, T. (2023). Explainable local and global models for fine-grained multimodal product recognition.
• Tiedemann, F., Sönströd, C., Löfström, T., Ohlson, N., Wikner, J., Johansson, U. (2023). Introducing Predictive Modelling in Industrial Organisations – Lessons Learned.
• Uddin, N., Löfström, T. (2023). Applications of Conformal Regression on Real-world Industrial Use Cases using Crepes and MAPIE. Proceedings of Machine Learning Research (PMLR)
• Alkhatib, A., Boström, H., Johansson, U. (2022). Assessing Explanation Quality by Venn Prediction. ML Research Press
• Dhanabalachandran, K., Hedblom, M., Beetz, M. (2022). Getting on top of things: Towards intelligent robotic object stacking through image-schematic reasoning. CEUR-WS
• Dhanabalachandran, K., Hedblom, M., Beetz, M. (2022). A balancing act: Ordering algorithm and image-schematic action descriptors for stacking objects by household robots. CEUR-WS
• Hedblom, M., Neuhaus, F. (2022). Visualising image schemas: A preliminary look at the Diagrammatic Image Schema Language (DISL). CEUR-WS
• Johansson, U., Boström, H., Nguyen, K., Luo, Z., Carlsson, L. (2022). Preface. ML Research Press
• Johansson, U., Löfström, T., Ståhl, N. (2022). Well-Calibrated Rule Extractors. ML Research Press
• Löfström, H., Hammar, K., Johansson, U. (2022). A meta survey of quality evaluation criteria in explanation methods. Cham: Springer
• Löfström, T., Ryasik, A., Johansson, U. (2022). Tutorial for using conformal prediction in KNIME. ML Research Press
• Ohlson, N., Riveiro, M., Bäckstrand, J. (2022). Identification of tasks to be supported by machine learning to reduce Sales & Operations Planning challenges in an engineer-to-order context. Amsterdam: IOS Press
• Pettersson, T., Oucheikh, R., Löfström, T. (2022). NLP Cross-Domain Recognition of Retail Products. New York: Association for Computing Machinery (ACM)
• Pomarlan, M., De Giorgis, S., Hedblom, M., Diab, M., Tsiogkas, N. (2022). Thinking in front of the box: Towards intelligent robotic action selection for navigation in complex environments using image-schematic reasoning. CEUR-WS
• Righetti, G., Porello, D., Troquard, N., Kutz, O., Hedblom, M., Galliani, P. (2022). Asymmetric Hybrids: Dialogues for Computational Concept Combination (Extended Abstract).
• Riveiro, M., Thill, S. (2022). The challenges of providing explanations of AI systems when they do not behave like users expect. New York: Association for Computing Machinery (ACM)
• Sattari, A., Johansson, U., Wilderoth, E., Jakupovic, J., Larsson-Green, P. (2022). The Interpretable Representation of Football Player Roles Based on Passing/Receiving Patterns. Springer
• Sweidan, D., Johansson, U., Gidenstam, A., Alenljung, B. (2022). Predicting Customer Churn in Retailing. Institute of Electrical and Electronics Engineers (IEEE)
• Aralikatte, R., Murrieta Bello, H., de Lhoneux, M., Hershcovich, D., Bollmann, M., Søgaard, A. (2021). How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task. Association for Computational Linguistics
• Arvidsson, S., Gabrielsson, P., Johansson, U. (2021). Texture Mapping of Flags onto Polandball Characters using Convolutional Neural Nets.
• Bollmann, M., Aralikatte, R., Murrieta Bello, H., Hershcovich, D., de Lhoneux, M., Søgaard, A. (2021). Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task. Association for Computational Linguistics
• Boström, H., Johansson, U., Löfström, T. (2021). Mondrian conformal predictive distributions. PMLR
• Botteghi, N., Alaa, K., Poel, M., Sirmacek, B., Brune, C., Mersha, A., Stramigioli, S. (2021). Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces. Institute of Electrical and Electronics Engineers (IEEE)
• Dhanabalachandran, K., Hassouna, V., Hedblom, M., Kümpel, M., Leusmann, N., Beetz, M. (2021). Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation. ACM Digital Library
• Johansson, U., Bostrom, H., Löfström, T. (2021). Investigating Normalized Conformal Regressors. Institute of Electrical and Electronics Engineers (IEEE)
• Johansson, U., Löfström, T., Boström, H. (2021). Well-Calibrated and Sharp Interpretable Multi-Class Models. Springer Science and Business Media Deutschland GmbH
• Johansson, U., Löfström, T., Boström, H. (2021). Calibrating multi-class models. PMLR
• Ohlson, N., Bäckstrand, J., Riveiro, M. (2021). Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context.
• Oualil, S., Oucheikh, R., El Kamili, M., Berrada, I. (2021). Benchmarking Classical and AI-based Caching Strategies in Internet of Vehicles. IEEE
• Oucheikh, R., Fri, M., Fedouaki, F., Hain, M. (2021). Deep anomaly detector based on spatio-temporal clustering for connected autonomous vehicles. Springer
• Peretz-Andersson, E., Lavesson, N., Bifet, A., Mikalef, P. (2021). AI Transformation in the Public Sector: Ongoing Research. Institute of Electrical and Electronics Engineers (IEEE)
• Pomarlan, M., Hedblom, M., Porzel, R. (2021). Panta Rhei: Curiosity-Driven Exploration to Learn the Image-Schematic Affordances of Pouring Liquids. CEUR-WS
• Righetti, G., Porello, D., Troquard, N., Kutz, O., Hedblom, M., Galliani, P. (2021). Asymmetric Hybrids: Dialogues for Computational Concept Combination. IOS Press
• Schuller, B., Virtanen, T., Riveiro, M., Rizos, G., Han, J., Mesaros, A., Drossos, K. (2021). Towards Sonification in Multimodal and User-Friendly Explainable Artificial Intelligence. Association for Computing Machinery
• Shaw, C., de Andrade Pereira, F., Hoare, C., Hammar, K., McNally, C., O’Donnell, J. (2021). Digital Interoperability for the Facilities Management Domain: a Review of Semantic Web-based Approaches.
• Stenhager, E., Lavesson, N. (2021). Hit Detection in Sports Pistol Shooting. Institute of Electrical and Electronics Engineers (IEEE)
• Annavarjula, V., Mbiydzenyu, G., Riveiro, M., Lavesson, N. (2020). Implicit user data in fashion recommendation systems. World Scientific
• Botteghi, N., Kamilaris, A., Sinai, L., Sirmacek, B. (2020). Multi-Agent Path Planning of Robotic Swarms in Agricultural Fields. Copernicus GmbH
• Botteghi, N., Obbink, R., Geijs, D., Poel, M., Sirmacek, B., Brune, C. ... Stramigioli, S. (2020). Low dimensional state representation learning with reward-shaped priors. Institute of Electrical and Electronics Engineers (IEEE)
• Botteghi, N., Sirmacek, B., Schulte, R., Poel, M., Brune, C. (2020). Reinforcement learning helps slam: Learning to build maps. International Society for Photogrammetry and Remote Sensing
• Hammar, K. (2020). Linked Data Creation with ExcelRDF. Cham: Springer
• Hammar, K., Kutz, O., Dimou, A., Hahmann, T., Hoehndorf, R., Masolo, C., Vita, R. (2020). Preface. CEUR-WS
• Johansson, U., Löfström, T. (2020). Well-calibrated and specialized probability estimation trees. Society for Industrial and Applied Mathematics
• Oucheikh, R., Fri, M., Fedouaki, F., Hain, M. (2020). Deep real-time anomaly detection for connected autonomous vehicles. Elsevier
• Shimizu, C., Hammar, K., Hitzler, P. (2020). Modular graphical ontology engineering evaluated. Cham: Springer
• Sweidan, D., Johansson, U., Gidenstam, A. (2020). Predicting returns in men's fashion. Singapore: World Scientific
• Tan, H., Tarasov, V., Fourlakidis, V., Diószegi, A. (2020). Data-driven modeling of mechanical properties of cast iron using fuzzy logic. Amsterdam: IOS Press
• Ventocilla, E., Martins, R., Paulovich, F., Riveiro, M. (2020). Progressive multidimensional projections: A process model based on vector quantization. Eurographics - European Association for Computer Graphics
• Ventocilla, E., Riveiro, M. (2020). A model for the progressive visualization of multidimensional data structure. Cham: Springer
• Westphal, F., Grahn, H., Lavesson, N. (2020). Representative image selection for data efficient word spotting. Springer
• Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H., Lavesson, N. (2019). Higher order mining for monitoring district heating substations. Institute of Electrical and Electronics Engineers (IEEE)
• Boström, H., Johansson, U., Vesterberg, A. (2019). Predicting with Confidence from Survival Data.
• García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms.
• Giri, C., Johansson, U., Löfström, T. (2019). Predictive modeling of campaigns to quantify performance in fashion retail industry. IEEE
• Hammar, K., Wallin, E., Karlberg, P., Hälleberg, D. (2019). The RealEstateCore Ontology. Cham: Springer
• Huhnstock, N., Karlsson, A., Riveiro, M., Steinhauer, H. (2019). An Infinite Replicated Softmax Model for Topic Modeling. Springer
• Johansson, U., Gabrielsson, P. (2019). Are Traditional Neural Networks Well-Calibrated?. IEEE
• Johansson, U., Löfström, T., Boström, H. (2019). Calibrating probability estimation trees using Venn-Abers predictors. Society for Industrial and Applied Mathematics
• Johansson, U., Löfström, T., Boström, H., Sönströd, C. (2019). Interpretable and Specialized Conformal Predictors.
• Johansson, U., Sonstrod, C., Löfström, T., Bostrom, H. (2019). Customized interpretable conformal regressors. Institute of Electrical and Electronics Engineers (IEEE)
• Shimizu, C., Hammar, K. (2019). CoModIDE – The Comprehensive Modular Ontology Engineering IDE. CEUR-WS
• Tan, H., Tarasov, V., Adlemo, A. (2019). Lessons Learned from an Application of Ontologies in Software Testing. CEUR-WS
• Tarasov, V., Seigerroth, U., Sandkuhl, K. (2019). Ontology development strategies in industrial contexts. Cham: Springer
• Tarasov, V., Seigerroth, U., Sandkuhl, K. (2019). Ontology Development Strategies in Industrial Contexts Workshops, Berlin, Germany, July 18-20, 2018, Revised Papers. Springer
• Tarasov, V., Tan, H., Adlemo, A. (2019). Automation of software testing process using ontologies. SciTePress
• Thill, S., Riveiro, M. (2019). Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems.
• Westphal, F., Lavesson, N., Grahn, H. (2019). A case for guided machine learning. Cham: Springer
• Westphal, F., Lavesson, N., Grahn, H. (2019). Learning character recognition with graph-based privileged information. IEEE
• Abghari, S., Boeva, V., Lavesson, N., Grahn, H., Gustafsson, J., Shaikh, J. (2018). Outlier Detection for Video Session Data Using Sequential Pattern Mining.
• Abghari, S., Boeva, V., Lavesson, N., Grahn, H., Ickin, S., Gustafsson, J. (2018). A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences. IEEE
• Adlemo, A., Hilletofth, P., Tarasov, V. (2018). Fuzzy logic based decision-support for reshoring decisions.
• Adlemo, A., Tan, H., Tarasov, V. (2018). Test case quality as perceived in Sweden. ACM Digital Library
• Adlemo, A., Tarasov, V., Hilletofth, P., Eriksson, D. (2018). Reshoring decision support in a Swedish context.
• Adlemo, A., Tarasov, V., Hilletofth, P., Eriksson, D. (2018). Knowledge intensive decision support for reshoring decisions. Kolding
• Alirezaie, M., Hammar, K., Blomqvist, E., Nyström, M., Ivanova, V. (2018). SmartEnv Ontology in E-care@home. CEUR-WS
• Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P., Lavesson, N. (2018). An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA).
• Boeva, V., Angelova, M., Lavesson, N., Rosander, O., Tsiporkova, E. (2018). Evolutionary clustering techniques for expertise mining scenarios. SciTePress
• García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2018). Hoeffding Trees with nmin adaptation. IEEE
• García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2018). How to Measure Energy Consumption in Machine Learning Algorithms.
• Johansson, U., Löfström, T., Sundell, H. (2018). Venn predictors using lazy learners. CSREA Press
• Johansson, U., Löfström, T., Sundell, H., Linusson, H., Gidenstam, A., Boström, H. (2018). Venn predictors for well-calibrated probability estimation trees.
• Linusson, H., Johansson, U., Boström, H., Löfström, T. (2018). Classification with reject option using conformal prediction. Springer
• Löfström, T., Johansson, U., Balkow, J., Sundell, H. (2018). A data-driven approach to online fitting services. World Scientific
• Sundell, H., Löfström, T., Johansson, U. (2018). Explorative multi-objective optimization of marketing campaigns for the fashion retail industry. World Scientific
• Westphal, F., Grahn, H., Lavesson, N. (2018). User Feedback and Uncertainty in User Guided Binarization. IEEE Computer Society
• Westphal, F., Lavesson, N., Grahn, H. (2018). Document Image Binarization Using Recurrent Neural Networks.
• Abghari, S., García Martín, E., Johansson, C., Lavesson, N., Grahn, H. (2017). Trend analysis to automatically identify heat program changes. Elsevier
• Ahlberg, E., Winiwarter, S., Boström, H., Linusson, H., Löfström, T., Norinder, U. ... Carlsson, L. (2017). Using conformal prediction to prioritize compound synthesis in drug discovery. Machine Learning Research
• García Martín, E., Lavesson, N., Grahn, H. (2017). Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree. Cham, Switzerland: Springer
• García-Martín, E., Lavesson, N. (2017). Is it ethical to avoid error analysis?.
• Hammar, K., Hitzler, P., Krisnadhi, A., Ławrynowicz, A., Nuzzolese, A., Solanki, M. (2017). Preface. IOS Press
• Hammar, K., Presutti, V. (2017). Template-Based Content ODP Instantiation. IOS Press
• Johansson, C., Bergkvist, M., Geysen, D., De Somer, O., Lavesson, N., Vanhoudt, D. (2017). Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms. Elsevier
• Johansson, U., Linusson, H., Löfström, T., Boström, H. (2017). Model-agnostic nonconformity functions for conformal classification. IEEE
• Karima, N., Hammar, K., Hitzler, P. (2017). How to Document Ontology Design Patterns. IOS Press
• Kusetogullari, H., Grahn, H., Lavesson, N. (2017). Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering. IEEE
• König, R., Johansson, U., Riveiro, M., Brattberg, P. (2017). Modeling golf player skill using machine learning. Springer
• Linusson, H., Norinder, U., Boström, H., Johansson, U., Löfström, T. (2017). On the calibration of aggregated conformal predictors. Machine Learning Research
• Tan, H., Adlemo, A., Tarasov, V., Johansson, M. (2017). Evaluation of an Application Ontology. CEUR-WS
• Resmini, A., Tan, H., Tarasov, V., Adlemo, A. (2016). #ViewFromTheOffice - Reconceptualizing the Workplace as an Information-based Ecosystem.
• Tan, H., Ismail, M., Tarasov, V., Adlemo, A., Johansson, M. (2016). Development and evaluation of a software requirements ontology. SciTePress
• Hammar, K. (2015). Ontology Design Patterns in WebProtégé. CEUR-WS
• Lantow, B., Sandkuhl, K., Tarasov, V. (2015). Ontology Reuse Engineering and Ontology Development. SciTePress
• Tan, H., Barakat, G., Tarasov, V. (2015). Translating XML Models into OWL Ontologies for Interoperability of Simulation Systems.
• Tan, H., Resmini, A., Tarasov, V., Adlemo, A. (2015). Workplace innovation in Swedish local organizations - technology aspect: BIS 2015 International Workshops, Poznań, Poland, June 24-26, 2015, Revised Papers.
• Tarasov, V., Tan, H., Adlemo, A., Ismail, M., Mats, J., Olsson, D. (2015). Ontology-based Software Test Case Generation (OSTAG). SciTePress
• Hammar, K. (2014). Ontology Design Pattern Property Specialisation Strategies. Berlin: Springer Berlin/Heidelberg
• Hammar, K. (2014). Ontology Design Patterns: Improving Findability and Composition. Berlin: Springer Berlin/Heidelberg
• Hammar, K. (2014). Ontology Design Patterns: Adoption Challenges and Solutions. EUR-WS
• Hammar, K. (2014). Reasoning Performance Indicators for Ontology Design Patterns. CEUR-WS
• Tan, H. (2014). A Method for Building FrameNet-like Corpus for the Biomedical Domain. Association for Computational Linguistics
• Lantow, B., Sandkuhl, K., Tarasov, V. (2013). Selecting Content Ontology Design Patterns for Ontology Quality Improvement. CEUR-WS
• Hammar, K. (2012). Ontology Design Patterns in Use: Lessons Learnt from an Ontology Engineering Case.
• Hammar, K. (2012). Modular Semantic CEP for Threat Detection. Mexico City: National Polytechnic Institute
• Krizhanovsky, A., Lin, F., Sandkuhl, K., Shilov, N., Smirnov, A., Tarasov, V. (2012). Logistics-as-a-service: ontology-based architecture and approach.
• Lin, F., Neshnega, L., Subba, B., Tarasov, V. (2012). A Framework for Context Automatic Integration in Ubiquitous Computing. IEEE
• Sandkuhl, K., Seigerroth, U., Tarasov, V. (2012). Context Modelling for Decision Making in Competence Supply: Experiences from a Military Case.
• Tarasov, V., Höglund, P., de Roos, P. (2012). Ontology-Based eService Enabling Collaboration of Researchers in Healthcare. International Information Management Corporation Limited
• Ali Fareedi, A., Tarasov, V. (2011). Modelling of the Ward Round Process in a Healthcare Unit. Springer Berlin/Heidelberg
• Hammar, K. (2011). DC Proposal: Towards an ODP Quality Model. Berlin: Springer
• Hammar, K. (2011). The State of Ontology Pattern Research. Riga, Latvia: Riga Technical University
• Khan, N., Carstensen, A., Sandkuhl, K., Tarasov, V. (2011). Transformation of Enterprise Model to Enterprise Ontology. Riga Technical University
• Tarasov, V., Krizhanovsky, A. (2011). Enrichment of Ontology-Based Competence Profiles with Semistructured Wiktionary Data. Riga Technical University
• Tarasov, V., Sandkuhl, K. (2011). On the Role of Competence Models for Business and IT Alignment in Network Organizations. Springer Berlin/Heidelberg
• Albertsen, T., Sandkuhl, K., Seigerroth, U., Tarasov, V. (2010). The Practice of Competence Modelling. Berlin: Springer
• Albertsen, T., Sandkuhl, K., Seigerroth, U., Tarasov, V. (2010). Modelling Network-based Defence: Success and Failure of an Enterprise Modelling Endeavour. Berlin: Springer
• Hammar, K., Sandkuhl, K. (2010). The State of Ontology Pattern Research: A Systematic Review of ISWC, ESWC and ASWC 2005–2009.
• Hammar, K., Tarasov, V., Lin, F. (2010). Information Reuse and Interoperability with Ontology Patterns and Linked Data: First Experiences from the ExpertFinder Project. Berlin: Springer
• Hugoson, M., Larsson, U., Tarasov, V. (2010). Interaction between Heterogeneous Autonomous Systems: Principles and Practice. Springer Berlin Heidelberg
• Sandkuhl, K., Tarasov, V. (2010). Comparison of Approaches for Competence Demand Modeling in Flexible Supply Networks. St, Petersburg, Russia
• Levashova, T., Sandkuhl, K., Shilov, N., Smirnov, A., Tarasov, V. (2009). Product Design Network Self-Contextualization: Enterprise Knowledge-Based Approach and Agent-Based Technological Framework. Berlin: Springer
• Sandkuhl, K., Smirnov, A., Mazalov, V., Vdovitsyn, V., Tarasov, V., Krizhanovsky, A. ... Ivashko, E. (2009). Context-Based Retrieval in Digital Libraries: Approach and Technological Framework. Petorzavodsk
• Sandkuhl, K., Tarasov, V. (2009). Modelling Competence Demand for Flexible Supply Networks. IFAC
• Kashevnik, A., Sandkuhl, K., Shilov, N., Smirnov, A., Tarasov, V. (2008). Business Community Creation Based on Competence Management. Amsterdam: IOS Press
• Albertsen, T., Sandkuhl, K., Tarasov, V. (2007). Towards Competence Modeling and Competence Matching for Network-based Defense.
• Tarasov, V., Albertsen, T., Kashevnik, A., Sandkuhl, K., Shilov, N., Smirnov, A. (2007). Ontology-Based Competence Management for Team Configuration. Berlin: Springer
• Sandkuhl, K., Lundqvist, M., Tarasov, V. (2006). COMPETENCE MODEL FOR COLLABORATIVE DESIGN.
• Tarasov, V., Sandkuhl, K., Henoch, B. (2006). Using Ontologies for Representation of Individual and Enterprise Competence Models. Piscataway, NJ.: IEEE Operations Center
• Blomqvist, E., Levashova, T., Öhgren, A., Sandkuhl, K., Smirnov, A., Tarasov, V. (2005). Configuration of Dynamic SME Supply Chains Based on Ontologies. Springer Berlin/Heidelberg
• Tarasov, V., Tarasov, V. (2003). Designing tools for testing with open-ended questions using Java technology. Joensuu: University of Joensuu
• Tarasov, V., Tarasov, V., Kyurshunov, A. (2003). Using XML and the IMS QTI standard for the development of assessment tools. Joensuu: University of Joensuu
• Tarasov, V. (2000). Multi-agent system architecture for supporting the process of distance learning. Joensuu: University of Joensuu
• Tarasov, V., Tarasov, V. (2000). Designing answer selection tests in JavaScript. Joensuu: University of Joensuu
• Mazalov, V., Vdovitsyn, V., Tarasov, V. (1999). Negotiation on data reallocation in distributed information systems. St. Petersburg
• Vdovitsyn, V., Tarasov, V. (1999). Multi-agent system for informational support of collaborative researchers work in a computer network. Petrozavodsk: Petrozavodsk State University
• Tarasov, V. (1998). Applying intelligent agent technology to create instruction programs. Petrozavodsk: Petrozavodsk State University
• Tarasov, V. (1998). Using intelligent agent technology for the development of a system teaching how to search for function limits. Joensuu: University of Joensuu
• Tarasov, V. (1997). Using explicit representation of inference search metarules for inference search control in expert systems. St. Petersburg
• Vdovitsyn, V., Tarasov, V. (1996). Applying KBS-technology to a pulp-and-paper mill control automation. Petrozavodsk

Conference proceedings (editor)

• Hedblom, M., Kutz, O. (2023). ISD7 2023, The Seventh Image Schema Day: Proceedings of the Seventh Image Schema Day, co-located with The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023). CEUR-WS
• Hedblom, M., Kantosalo, A., Confalonieri, R., Kutz, O., Veale, T. (2022). Proceedings of the 13th International Conference on Computational Creativity. Association for Computational Creativity (ACC)
• Hedblom, M., Kutz, O. (2022). ISD6 The Image Schema Day 2022. CEUR-WS
• Hedblom, M., Kutz, O. (2022). Proceedings of the Sixth Image Schema Day 2022: Jönköping, Sweden, March 24-25th, 2022. CEUR-WS
• Righetti, G., Hedblom, M., Kutz, O. (2022). Proceedings of the Joint Ontology Workshops 2022, Episode VIII: The Svear Sommar of Ontology: CAOS VI: 6th International Workshop on Cognition and Ontologies. CEUR-WS
• Sales, T., Hedblom, M., Tan, H. (2022). Proceedings of the Joint Ontology Workshops 2022, Episode VIII: The Svear Sommar of Ontology. CEUR-WS
• Hammar, K., Shimizu, C., McGinty, H., Asprino, L., Carriero, V. (2021). WOP 2021: Workshop on Ontology Design and Patterns 2021: Proceedings of the 12th Workshop on Ontology Design and Patterns (WOP 2021) co-located with the 20th International Semantic Web Conference (ISWC 2021), online, October 24, 2021. CEUR-WP
• Hammar, K., Kutz, O., Dimou, A., Hahmann, T., Hoehndorf, R., Masolo, C., Vita, R. (2020). JOWO 2020: The Joint Ontology Workshops: Proceedings of the Joint Ontology Workshops co-located with the Bolzano Summer of Knowledge (BOSK 2020). CEUR-WS
• Hitzler, P., Fernández, M., Janowicz, K., Zaveri, A., Gray, A., Lopez, V. ... Hammar, K. (2019). The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019, Proceedings. Cham: Springer
• Janowicz, K., Krisnadhi, A., Poveda Villalón, M., Hammar, K., Shimizu, C. (2019). WOP 2019: Workshop on Ontology Design and Patterns 2019: Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019), co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 2019. CEUR-WP
• Skjæveland, M., Hu, Y., Hammar, K., Svátek, V., Ławrynowicz, A. (2018). WOP 2018: Workshop on Ontology Design and Patterns. CEUR Workshop Proceedings
• Hammar, K., Hitzler, P., Krisnadhi, A., Ławrynowicz, A., Nuzzolese, A., Solanki, M. (2017). Advances in Ontology Design and Patterns. IOS Press
• Gangemi, A., Gruninger, M., Hammar, K., Lefort, L., Presutti, V., Scherp, A. (2014). Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns. CEUR-WS
• Hammar, K., García-Crespo, A., Gómez Berbís, J., Radzimski, M., Sánchez Cervantes, J., Coppens, S. ... Vander Sande, M. (2014). WaSABi-FEOSW 2014: Joint Proceedings of WaSABi 2014 and FEOSW 2014. CEUR-WS
• Coppens, S., Hammar, K., Knuth, M., Neumann, M., Ritze, D., Sack, H., Vander Sande, M. (2013). WaSABi 2013: Semantic Web Enterprise Adoption and Best Practice: Proceedings of the Workshop on Semantic Web Enterprise Adoption and Best Practice Co-located with 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 22, 2013. CEUR-WS
• Blomqvist, E., Gangemi, A., Hammar, K., Suárez-Figueroa, M. (2012). WOP 2012: Proceedings of the 3rd Workshop on Ontology Patterns. CEUR-WS

Doctoral thesis

• Hammar, K. (2017). Content Ontology Design Patterns: Qualities, Methods, and Tools. (Doctoral thesis, Linköping: Linköping University Electronic Press)

Licentiate thesis

• Hammar, K. (2013). Towards an Ontology Design Pattern Quality Model. Linköping: Linköping University Electronic Press

Other publication

• Löfström, H., Löfström, T., Johansson, U., Sönströd, C. (null). Calibrated explanations with uncertainty information and counterfactuals.

Report

• Löfström, T., Ralsmark, H., Johansson, U. (2021). Collusion in algorithmic pricing. Stockholm: Konkurrensverket
• Sandkuhl, K., Tarasov, V., Mazalov, V., Smirnov, A. (2012). Context based Retrieval in Digital Libraries: Continuing PhD Education and Experimentation (CoReLib2): Final project report. Jönköping: Jönköping University, School of Engineering
• Albertsen, T., Seigerroth, U., Sandkuhl, K., Tarasov, V. (2009). Competence demand modeling: case study. Jönköping University: Jönköping University
• Albertsen, T., Tarasov, V., Sandkuhl, K., Seigerroth, U. (2009). Competence modeling and matching: frame concept. Jönköping University: Jönköping University
• Albertsen, T., Tarasov, V., Sandkuhl, K., Seigerroth, U. (2009). Modellering av 312. luftburna kompaniet: KOMO - Projektrapport. Jönköping University
• Sandkuhl, K., Tarasov, V., Albertsen, T., Seigerroth, U. (2009). Competence demand modeling based on enterprise models: KOMO - project report. Jönköping University: Jönköping University
• Sandkuhl, K., Tarasov, V., Mazalov, V., Smirnov, A. (2009). Context based Retrieval in Digital Libraries (CoReLib): Final project report. Jönköping: School of Engineering at Jönköping University
• Sandkuhl, K., Henoch, B., Le Hieu, H., Hugoson, M., Larsson, U., Tarasov, V. (2007). ICT-Support for Formation of Business Relationships with Developing Countries Based on Immigrant Competence. Phase 2: Integrating Competence Models into Chamber Trade: Final project report. Jönköping: Jönköping University
• Sandkuhl, K., Hao, D., Henoch, B., Hugoson, M., Larsson, U., Roren, H., Tarasov, V. (2005). ICT-Support for Formation of Business Relationships with Developing Countries Based on Immigrant Competence. Pilot Study Vietnam: Final project report. Jönköping: Jönköping University