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Xu, Z., Xu, W., Wang, R., Chen, J., Qi, C., & Lukasiewicz, T. (2025). Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 2206–2220. https://doi.org/10.1109/TNNLS.2023.3343391 ( reposiTUm)

Xu, Z., Liu, Y., Xu, G., & Lukasiewicz, T. (2025). Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking. IEEE Transactions on Medical Imaging, 44(1), 180–193. https://doi.org/10.1109/TMI.2024.3436608 ( reposiTUm)

Xu, Z., Wang, H., Yang, R., Yang, Y., Liu, W., & Lukasiewicz, T. (2025). Aggregated Mutual Learning between CNN and Transformer for semi-supervised medical image segmentation. Knowledge-Based Systems, 311, Article 113005. https://doi.org/10.1016/j.knosys.2025.113005 ( reposiTUm)

Yu, M., Xu, Z., & Lukasiewicz, T. (2025). A general survey on medical image super-resolution via deep learning. Computers in Biology and Medicine, 193, Article 110345. https://doi.org/10.1016/j.compbiomed.2025.110345 ( reposiTUm)

Ceylan, İ. İ., Lukasiewicz, T., Malizia, E., & Vaicenavičius, A. (2025). Explanations for query answers under existential rules. Artificial Intelligence, 341, Article 104294. https://doi.org/10.1016/j.artint.2025.104294 ( reposiTUm)

Xu, Z., Wang, S., Xu, G., Liu, Y., Yu, M., Zhang, H., Lukasiewicz, T., & Gu, J. (2024). Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning. Computers in Biology and Medicine, 169, Article 107877. https://doi.org/10.1016/J.COMPBIOMED.2023.107877 ( reposiTUm)

Collins, K. M., Jiang, A. Q., Frieder, S., Wong, L., Zilka, M., Bhatt, U., Lukasiewicz, T., Wu, Y., Tenenbaum, J. B., Hart, W., Gowers, T., Li, W., Weller, A., & Jamnik, M. (2024). Evaluating Language Models for Mathematics through Interactions. Proceedings of the National Academy of Sciences of the United States of America, 121(24), Article e2318124121. https://doi.org/10.1073/pnas.2318124121 ( reposiTUm)

Mahon, L., Sha, L., & Lukasiewicz, T. (2024). Correcting Flaws in Common Disentanglement Metrics. Transactions on Machine Learning Research, 2024. ( reposiTUm)

Xu, Z., Tang, J., Qi, C., Yao, D., Liu, C., Zhan, Y., & Lukasiewicz, T. (2024). Cross-domain attention-guided generative data augmentation for medical image analysis with limited data. Computers in Biology and Medicine, 168, Article 107744. https://doi.org/10.1016/J.COMPBIOMED.2023.107744 ( reposiTUm)

Sha, L., & Thomas Lukasiewicz. (2024). Text attribute control via closed-loop disentanglement. Transactions of the Association for Computational Linguistics, 12. https://doi.org/10.1162/tacl_a_00640 ( reposiTUm)

Xu, Z., Yu, Z., Zhang, H., Chen, J., Gu, J., Lukasiewicz, T., & Leung, V. C. M. (2024). PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention. IEEE Transactions on Network Science and Engineering, 11(1), 427–438. https://doi.org/10.1109/TNSE.2023.3300744 ( reposiTUm)

Kocijan, V., Jang, M., & Lukasiewicz, T. (2024). Pre-training and diagnosing knowledge base completion models. Artificial Intelligence, 329, Article 104081. https://doi.org/10.1016/j.artint.2024.104081 ( reposiTUm)

Giunchiglia, E., Tatomir, A., Stoian, M. C., & Lukasiewicz, T. (2024). CCN⁺: A Neuro-symbolic Framework for Deep Learning with Requirements  . International Journal of Approximate Reasoning, 171, Article 109124. https://doi.org/10.1016/j.ijar.2024.109124 ( reposiTUm)

Song, Y., Millidge, B., Salvatori, T., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2024). Inferring neural activity before plasticity as a foundation for learning beyond backpropagation. Nature Neuroscience. https://doi.org/10.1038/s41593-023-01514-1 ( reposiTUm)

Mahon, L., & Lukasiewicz, T. (2024). Minimum description length clustering to measure meaningful image complexity. Pattern Recognition, 145, Article 109889. https://doi.org/10.1016/j.patcog.2023.109889 ( reposiTUm)

Zhang, J., Zhang, S., Shen, X., Lukasiewicz, T., & Xu, Z. (2024). Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 76–95. https://doi.org/10.1109/TMI.2023.3290356 ( reposiTUm)

Kocijan, V., Davis, E., Lukasiewicz, T., Marcus, G., & Morgenstern, L. (2023). The Defeat of the Winograd Schema Challenge. Artificial Intelligence, 325, Article 103971. https://doi.org/10.1016/j.artint.2023.103971 ( reposiTUm)

Yuan, D., Liu, Y., Xu, Z., Zhan, Y., Chen, J., & Lukasiewicz, T. (2023). Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing. Computers in Biology and Medicine, 153, Article 106487. https://doi.org/10.1016/j.compbiomed.2022.106487 ( reposiTUm)

Yuan, D., Xu, Z., Tian, B., Wang, H., Zhan, Y., & Lukasiewicz, T. (2023). μ-Net: Medical image segmentation using efficient and effective deep supervision. Computers in Biology and Medicine, 160, Article 106963. https://doi.org/10.1016/j.compbiomed.2023.106963 ( reposiTUm)

Xu, Z., Zhang, X., Zhang, H., Liu, Y., Zhan, Y., & Lukasiewicz, T. (2023). EFPN: Effective medical image detection using feature pyramid fusion enhancement. Computers in Biology and Medicine, 163, Article 107149. https://doi.org/https://doi.org/10.1016/j.compbiomed.2023.107149 ( reposiTUm)

Xu, Z., Li, T., Liu, Y., Zhan, Y., Chen, J., & Lukasiewicz, T. (2023). PAC-Net: Multi-pathway FPN with position attention guided connections and vertex distance IoU for 3D medical image detection. Frontiers in Bioengineering and Biotechnology, 11, Article 1049555. https://doi.org/10.3389/fbioe.2023.1049555 ( reposiTUm)

Yu, M., Guo, M., Zhang, S., Zhan, Y., Zhao, M., Lukasiewicz, T., & Xu, Z. (2023). RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising. Computers in Biology and Medicine, 167, Article 107632. https://doi.org/10.1016/j.compbiomed.2023.107632 ( reposiTUm)

Tang, M., Salvatori, T., Millidge, B., Song, Y., Lukasiewicz, T., & Bogacz, R. (2023). Recurrent predictive coding models for associative memory employing covariance learning. PLoS Computational Biology, 19(4), e1010719. https://doi.org/10.1371/journal.pcbi.1010719 ( reposiTUm)

Li, Y., Mamouei, M., Salimi-Khorshidi, G., Rao, S., Hassaine, A., Canoy, D., Lukasiewicz, T., & Rahimi, K. (2023). Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records. IEEE Journal of Biomedical and Health Informatics, 27(2), 1106–1117. https://doi.org/10.1109/JBHI.2022.3224727 ( reposiTUm)

Giunchiglia, E., Stoian, M. C., Khan, S., Cuzzolin, F., & Lukasiewicz, T. (2023). ROAD-R: the autonomous driving dataset with logical requirements. Machine Learning, 112, 3261–3291. https://doi.org/10.1007/s10994-023-06322-z ( reposiTUm)

Sha, L., Camburu, O.-M., & Lukasiewicz, T. (2023). Rationalizing predictions by adversarial information calibration. Artificial Intelligence, 315, 103828. https://doi.org/https://doi.org/10.1016/j.artint.2022.103828 ( reposiTUm)

Frieder, S., Berner, J., Petersen, P., & Lukasiewicz, T. (2023). Large Language Models for Mathematicians. Internationale Mathematische Nachrichten, 254, 1–20. http://hdl.handle.net/20.500.12708/192474 ( reposiTUm)

Xu, Z., Tian, B., Liu, S., Wang, X., Yuan, D., Gu, J., Chen, J., Lukasiewicz, T., & Leung, V. C. M. (2023). Collaborative Attention Guided Multi-Scale Feature Fusion Network for Medical Image Segmentation. IEEE Transactions on Network Science and Engineering, 1–15. https://doi.org/10.1109/TNSE.2023.3332810 ( reposiTUm)

Jang, M., & Thomas Lukasiewicz. (2022). NoiER: An Approach for Training more Reliable Fine-Tuned Downstream Task Models. IEEE/ACM Transactions on Audio, Speech and Language Processing, 30, 2514–2525. https://doi.org/10.1109/TASLP.2022.3193292 ( reposiTUm)

Eiter, T., Ianni, G., Lukasiewicz, T., & Schindlauer, R. (2011). Well-founded semantics for description logic programs in the Semantic Web. ACM Transactions on Computational Logic, 12(2), 1–41. https://doi.org/10.1145/1877714.1877717 ( reposiTUm)

Fazzinga, B., & Lukasiewicz, T. (2010). Semantic search on the Web. Semantic Web: Interoperability, Usability, Applicability, 1(1/2), 89–96. http://hdl.handle.net/20.500.12708/167954 ( reposiTUm)

Lukasiewicz, T. (2010). A novel combination of answer set programming with description logics for the Semantic Web. IEEE Transactions on Knowledge and Data Engineering, 22(11), 1577–1592. https://doi.org/10.1109/tkde.2010.111 ( reposiTUm)

Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2009). Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-00685-2 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2009). Description Logic Programs under Probabilistic Uncertainty and Fuzzy Vagueness. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 50(6), 837–853. https://doi.org/10.1016/j.ijar.2009.03.004 ( reposiTUm)

Iocchi, L., Lukasiewicz, T., Nardi, D., & Rosati, R. (2009). Reasoning about Actions with Sensing under Qualitative and Probabilistic Uncertainty. ACM Transactions on Computational Logic, 10(1), 1–41. https://doi.org/10.1145/1459010.1459015 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2008). Tightly Coupled Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. International Journal on Semantic Web and Information Systems, 4(3), 68–89. http://hdl.handle.net/20.500.12708/170863 ( reposiTUm)

Lukasiewicz, T. (2008). Expressive Probabilistic Description Logics. Artificial Intelligence, 172(6–7), 852–883. https://doi.org/10.1016/j.artint.2007.10.017 ( reposiTUm)

Lukasiewicz, T. (2008). Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. Fundamenta Informaticae, 82(3), 289–310. http://hdl.handle.net/20.500.12708/170864 ( reposiTUm)

Schellhase, J., & Lukasiewicz, T. (2008). Using Search Strategies and a Description Logic Paradigm with Conditional Preferences for Literature Search. International Journal of Metadata, Semantics and Ontologies, 3(1), 68. https://doi.org/10.1504/ijmso.2008.021206 ( reposiTUm)

Lukasiewicz, T. (2008). Probabilistic Description Logic Programs under Inheritance with Overriding for the Semantic Web. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 49(1), 18–34. https://doi.org/10.1016/j.ijar.2007.08.005 ( reposiTUm)

Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2008). Combining Answer Set Programming with Description Logics for the Semantic Web. Artificial Intelligence, 172(12–13), 1495–1539. https://doi.org/10.1016/j.artint.2008.04.002 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2008). Managing Uncertainty and Vagueness in Description Logics for the Semantic Web. Journal of Web Semantics, 6(4), 291–308. https://doi.org/10.1016/j.websem.2008.04.001 ( reposiTUm)

Lukasiewicz, T. (2008). Logical approaches to imprecise probabilities. International Journal of Approximate Reasoning, 49(1), 1–2. https://doi.org/10.1016/j.ijar.2007.08.004 ( reposiTUm)

Cano, A., Cozman, F. G., & Lukasiewicz, T. (2007). Reasoning with imprecise probabilities. International Journal of Approximate Reasoning, 44(3), 197–199. https://doi.org/10.1016/j.ijar.2006.09.001 ( reposiTUm)

Lukasiewicz, T. (2007). Probabilistic Description Logic Programs. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 45(2), 288–307. https://doi.org/10.1016/j.ijar.2006.06.012 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2007). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies. Journal of Web Semantics, 5(3), 180–194. https://doi.org/10.1016/j.websem.2007.06.001 ( reposiTUm)

Lukasiewicz, T. (2007). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Complexity, Algorithms, and Implementation. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 44(3), 301–321. https://doi.org/10.1016/j.ijar.2006.07.015 ( reposiTUm)

Eiter, T., & Lukasiewicz, T. (2006). Causes and Explanations in the Structural-Model Approach: Tractable Cases. Artificial Intelligence, 170(6–7), 542–580. http://hdl.handle.net/20.500.12708/173415 ( reposiTUm)

Biazzo, V., Gilio, A., Lukasiewicz, T., & Sanfilippo, G. (2005). Probabilistic Logic under Coherence: Complexity and Algorithms. Annals of Mathematics and Artificial Intelligence, ONLINE FIRST(Online First). http://hdl.handle.net/20.500.12708/173380 ( reposiTUm)

Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Reasoning under Variable-Strength Inheritance with Overriding. Synthese, 146(1–2), 153–169. http://hdl.handle.net/20.500.12708/173382 ( reposiTUm)

Lukasiewicz, T. (2005). Weak Nonmonotonic Probabilistic Logics. Artificial Intelligence, 168(1–2), 119–161. http://hdl.handle.net/20.500.12708/173381 ( reposiTUm)

Pinchetti, L., Qi, C., Lokshyn, O., Emde, C., M’Charrak, A., Tang, M., Simon, F., Menzat, B. I., Oliviers, G., Bogacz, R., Lukasiewicz, T., & Salvatori, T. (2025). Benchmarking Predictive Coding Networks - Made Simple. In The Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April 24-28, 2025. Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore. http://hdl.handle.net/20.500.12708/223683 ( reposiTUm)

Emde, C., Paren, A. J., Arvind, P., Kayser, M. G., Rainforth, T., Lukasiewicz, T., Torr, P., & Bibi, A. (2025). Shh, don’t say that! Domain Certification in LLMs. In The Thirteenth International Conference on Learning Representations,{ICLR} 2025, Singapore, April 24-28, 2025. Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore. ( reposiTUm)

Emde, C., Pinto, F., Lukasiewicz, T., Torr, P., & Bibi, A. (2025). Towards Certification of Uncertainty Calibration under AdversarialAttacks. In The Thirteenth International Conference on Learning Representations,{ICLR} 2025, Singapore, April 24-28, 2025. Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore. ( reposiTUm)

Frieder, S., Pinchetti, L., & Lukasiewicz, T. (2024). Bad Predictive Coding Activation Functions. In The Second Tiny Papers Track at ICLR 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210293 ( reposiTUm)

Tommaso Salvatori, Song, Y., Yordanov, Y., Millidge, B., Sha, L., Emde, C., Xu, Z., Bogacz, R., & Thomas Lukasiewicz. (2024). A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks. In The Twelfth International Conference on Learning Representations (p. 25). http://hdl.handle.net/20.500.12708/211106 ( reposiTUm)

Stoian, M. C., Tatomir, A., Lukasiewicz, T., & Giunchiglia, E. (2024). PiShield: A NeSy Framework for Learning with Requirements. In K. Larson (Ed.), IJCAI ’24: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 8805–8809). Association for Computing Machinery. https://doi.org/10.24963/ijcai.2024/1037 ( reposiTUm)

Mahon, L., & Lukasiewicz, T. (2024). Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation. In Proceedings of the 38th AAAI Conference on Artificial Intelligence : AAAI-24 Technical Tracks 13 (pp. 14281–14288). AAAI Press. https://doi.org/10.1609/aaai.v38i13.29340 ( reposiTUm)

Stoian, M. C., Dyrmishi, S., Cordy, M., Lukasiewicz, T., & Giunchiglia, E. (2024). How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. In The Twelfth International Conference on Learning Representations. 12th International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210296 ( reposiTUm)

Kayser, M., Menzat, B. I., Emde, C., Bercean, B., Novak, A., Espinosa, A., Papiez, B. W., Gaube, S., Lukasiewicz, T., & Camburu, O.-M. (2024). Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18891–18919). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1051 ( reposiTUm)

Frieder, S., Olšák, M., Berner, J., & Lukasiewicz, T. (2024). The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs. In The Second Tiny Papers Track at ICLR 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210292 ( reposiTUm)

Kocijan, V., Davis, E., Lukasiewicz, T., Marcus, G., & Morgenstern, L. (2024). The Defeat of the Winograd Schema Challenge (Abstract Reprint). In AAAI-24 Special Track AI for Social Impact, Senior Member Presentations, New Faculty Highlights, Journal Track (pp. 22703–22703). AAAI Press. https://doi.org/10.1609/AAAI.V38I20.30603 ( reposiTUm)

Salvatori, T., Pinchetti, L., M’Charrak, A., Millidge, B., & Lukasiewicz, T. (2024). Predictive Coding beyond Correlations. In Forty-first International Conference on Machine Learning. Forty-first International Conference on Machine Learning, ICML 2024, Wien, Austria. http://hdl.handle.net/20.500.12708/210295 ( reposiTUm)

Atanasova, P., Camburu, O.-M., Lioma, C., Lukasiewicz, T., Simonsen, J. G., & Augenstein, I. (2023). Faithfulness Tests for Natural Language Explanations. In Association for Computational Linguistics (Ed.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 283–294). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.25 ( reposiTUm)

Xu, G., Wang, S., Lukasiewicz, T., & Xu, Z. (2023). Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation. In 2023 IEEE International Conference on Multimedia and Expo (ICME) (pp. 2285–2290). IEEE. https://doi.org/10.1109/ICME55011.2023.00390 ( reposiTUm)

Zhang, H., Xu, Z., Yao, D., Zhang, S., Chen, J., & Thomas Lukasiewicz. (2023). Multi-Head Feature Pyramid Networks for Breast Mass Detection. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes, Greece. IEEE. https://doi.org/10.1109/ICASSP49357.2023.10095967 ( reposiTUm)

Lukasiewicz, T., Malizia, E., & Molinaro, C. (2023). Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- under Preferred Repairs. In P. Marquis, T. C. Son, & G. Kern-Isberner (Eds.), Proceedings of 20th International Conference on Principles of Knowledge Representation and Reasoning (pp. 472–481). IJCAI Organization. https://doi.org/10.24963/kr.2023/46 ( reposiTUm)

Jang, M., & Lukasiewicz, T. (2023). Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 8496–8510). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.527 ( reposiTUm)

Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., & Bogacz, R. (2023). Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning. In The Eleventh International Conference on Learning Representations, ICLR 2023 (pp. 1–14). http://hdl.handle.net/20.500.12708/192480 ( reposiTUm)

Jang, M., & Lukasiewicz, T. (2023). Consistency Analysis of ChatGPT. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 15970–15985). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.991 ( reposiTUm)

Stoian, M. C., Giunchiglia, E., & Lukasiewicz, T. (2023). Exploiting T-norms for Deep Learning in Autonomous Driving. In A. S. d’Avila Garcez, T. R. Besold, M. Gori, & E. Jimenez-Ruiz (Eds.), Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023) (pp. 369–380). http://hdl.handle.net/20.500.12708/193631 ( reposiTUm)

Xie, Z., & Lukasiewicz, T. (2023). An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models. In In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 15730–15745). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.876 ( reposiTUm)

Millidge, B., Song, Y., Salvatori, T., Lukasiewicz, T., & Bogacz, R. (2023). A Theoretical Framework for Inference and Learning in Predictive Coding Networks. In The Eleventh International Conference on Learning Representations, ICLR 2023 (pp. 1–24). http://hdl.handle.net/20.500.12708/192478 ( reposiTUm)

Zhongbin, X., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2023). Counter−GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns. In A. Vlachos & Isabelle Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 3761–3773). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.272 ( reposiTUm)

Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023). KNOW How to Make Up Your Mind! Adversarially Detecting and Remedying Inconsistencies in Natural Language Explanations. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 540–553). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.47 ( reposiTUm)

Salvatori, T., Millidge, B., Song, Y., Bogacz, R., & Lukasiewicz, T. (2023). Associative Memories in the Feature Space. In K. Gal, A. Nowé, & G. J. Nalepa (Eds.), 26th European Conference on Artificial Intelligence, September 30–October 4, 2023, Kraków, Poland – Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023) (pp. 2065–2072). IOS Press. https://doi.org/10.3233/FAIA230500 ( reposiTUm)

Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P., & Berner, J. (2023). Mathematical Capabilities of ChatGPT. In Advances in Neural Information Processing Systems 36 pre-proceedings (NeurIPS 2023). 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the). http://hdl.handle.net/20.500.12708/194133 ( reposiTUm)

Wang, J., Massiceti, D., Hu, X., Pavlovic, V., & Lukasiewicz, T. (2023). NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning. http://hdl.handle.net/20.500.12708/192515 ( reposiTUm)

Mahon, L., & Lukasiewicz, T. (2023). Efficient Deep Clustering of Human Activities and How to Improve Evaluation. In E. Khan & M. Gönen (Eds.), Proceedings of The 14th Asian Conference on Machine Learning (pp. 722–737). http://hdl.handle.net/20.500.12708/193628 ( reposiTUm)

Wang, X., Wang, R., Tian, B., Zhang, J., Zhang, S., Chen, J., Lukasiewicz, T., & Xu, Z. (2023). MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes, Greece. IEEE. https://doi.org/10.1109/ICASSP49357.2023.10094657 ( reposiTUm)

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Jang, M., Mtumbuka, F., & Lukasiewicz, T. (2022). Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence. In Findings of the Association for Computational Linguistics: NAACL 2022 (pp. 2030–2042). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-naacl.156 ( reposiTUm)

Lukasiewicz, T., Malizia, E., & Molinaro, C. (2022). Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 2705–2711). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/375 ( reposiTUm)

Frieder, S., & Lukasiewicz, T. (2022). (Non-)Convergence Results for Predictive Coding Networks. In Proceedings of the 39th International Conference on Machine Learning (pp. 6793–6810). http://hdl.handle.net/20.500.12708/187543 ( reposiTUm)

Millidge, B., Salvatori, T., Song, Y., Bogacz, R., & Lukasiewicz, T. (2022). Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (pp. 5538–5545). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/774 ( reposiTUm)

Jang, M., Kwon, D. S., & Lukasiewicz, T. (2022). BECEL: Benchmark for Consistency Evaluation of Language Models. In N. Calzolari, C.-R. Huang, & H. Kim (Eds.), Proceedings of the 29th International Conference on Computational Linguistics (pp. 3680–3696). International Committee on Computational Linguistics. http://hdl.handle.net/20.500.12708/192675 ( reposiTUm)

Salvatori, T., Pinchetti, L., Millidge, B., Song, Y., Bao, T., Bogacz, R., & Lukasiewicz, T. (2022). Learning on Arbitrary Graph Topologies via Predictive Coding. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 38232–38244). Neural information processing systems foundation. http://hdl.handle.net/20.500.12708/192475 ( reposiTUm)

Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Image-to-Image Translation with Text Guidance. In The 33rd British Machine Vision Conference Proceedings (pp. 1–14). http://hdl.handle.net/20.500.12708/193629 ( reposiTUm)

Majumder, B. P., Camburu, O.-M., Lukasiewicz, T., & McAuley, J. (2022). Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. In K. Chaudhuri, S. Jegelka, & L. Song (Eds.), Proceedings of the 39th International Conference on Machine Learning (pp. 14786–14801). MLResearch Press. http://hdl.handle.net/20.500.12708/192473 ( reposiTUm)

Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 5478–5485). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/767 ( reposiTUm)

Pinchetti, L., Salvatori, T., Yordanov, Y., Millidge, B., Song, Y., & Lukasiewicz, T. (2022). Predictive Coding beyond Gaussian Distributions. In S. Koyejo, S. Mohamed, & A. Agarwal (Eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 1280–1293). http://hdl.handle.net/20.500.12708/192691 ( reposiTUm)

Cheng, Z., Wu, L., Thomas Lukasiewicz, Emanuel Sallinger, & Georg Gottlob. (2022). Democratizing Financial Knowledge Graph Construction by Mining Massive Brokerage Research Reports. In M. Ramanath & T. Palpanas (Eds.), Proceedings of the Workshops of the EDBT/ICDT 2022 Joint Conference. http://hdl.handle.net/20.500.12708/192765 ( reposiTUm)

Kayser, M., Emde, C., Camburu, O.-M., Parsons, G., Papiez, B., & Lukasiewicz, T. (2022). Explaining Chest X-Ray Pathologies in Natural Language. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (pp. 701–713). https://doi.org/10.1007/978-3-031-16443-9_67 ( reposiTUm)

Yordanov, Y., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2022). Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup. In Y. Goldberg, K. Zornitsa, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 3486–3501). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.255 ( reposiTUm)

Mtumbuka, F., & Lukasiewicz, T. (2022). Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 5972–5987). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.401 ( reposiTUm)

Li, B., & Lukasiewicz, T. (2022). Learning to Model Multimodal Semantic Alignment for Story Visualization. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 4741–4747). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.346 ( reposiTUm)

Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Clustering Generative Adversarial Networks for Story Visualization. In MM ’22: Proceedings of the 30th ACM International Conference on Multimedia (pp. 769–778). Association for Computing Machinery. https://doi.org/10.1145/3503161.3548034 ( reposiTUm)

Li, B., Torr, P. H. S., & Lukasiewicz, T. (2022). Memory-Driven Text-to-Image Generation. In The 33rd British Machine Vision Conference Proceedings. 33rd British Machine Vision Conference, London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/193654 ( reposiTUm)

Millidge, B., Salvatori, T., Song, Y., Lukasiewicz, T., & Bogacz, R. (2022). Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models. In Proceedings of the 39th International Conference on Machine Learning (pp. 15561–15583). http://hdl.handle.net/20.500.12708/192477 ( reposiTUm)

Wang, J., Lukasiewicz, T., Massiceti, D., Hu, X., Pavlovic, V., & Neophytou, A. (2022). NP-Match: When Neural Processes meet Semi-Supervised Learning. In Proceedings of the 39th International Conference on Machine Learning (pp. 22919–22934). PMLR. http://hdl.handle.net/20.500.12708/192517 ( reposiTUm)

Eiter, T., Lukasiewicz, T., & Predoiu, L. (2016). Generalized Consistent Query Answering under Existential Rules. In J. P. Delgrande & F. Wolter (Eds.), Proceedings, Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2016) (pp. 359–368). http://hdl.handle.net/20.500.12708/56833 ( reposiTUm)

Lukasiewicz, T., Martinez, M. V., Pieris, A., & Simari, G. I. (2015). From Classical to Consistent Query Answering under Existential Rules. In Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015 (p. 6). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/56409 ( reposiTUm)

Lukasiewicz, T., Martinez, M. V., Pieris, A., & Simari, G. I. (2015). From Classical to Consistent Query Answering under Existential Rules. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 1546–1552). AAAI Press. https://doi.org/10.1609/aaai.v29i1.9414 ( reposiTUm)

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Fazzinga, B., Gianforme, G., Gottlob, G., & Lukasiewicz, T. (2010). Semantic Web search based on ontological conjunctive queries. In S. Link & H. Prade (Eds.), Foundations of Information and Knowledge Systems (pp. 153–172). Springer LNCS. https://doi.org/10.1007/978-3-642-11829-6_12 ( reposiTUm)

d´Amato, C., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2010). Combining Semantic Web search with the power of inductive reasoning. In A. Deshpande & A. Hunter (Eds.), Scalable Uncertainty Management (pp. 137–150). Springer LNCS. https://doi.org/10.1007/978-3-642-15951-0_17 ( reposiTUm)

Calì, A., Gottlob, G., Lukasiewicz, T., Marnette, B., & Pieris, A. (2010). Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications. In J.-P. Jouannaud (Ed.), 2010 25th Annual IEEE Symposium on Logic in Computer Science. IEEE Computer Society. https://doi.org/10.1109/lics.2010.27 ( reposiTUm)

Pichler, R., Polleres, A., Skritek, S., & Woltran, S. (2010). Redundancy Elimination on RDF Graphs in the Presence of Rules, Constraints, and Queries. In P. Hitzler & T. Lukasiewicz (Eds.), Web Reasoning and Rule Systems (pp. 133–148). Lecture Notes/ Springer. https://doi.org/10.1007/978-3-642-15918-3_11 ( reposiTUm)

d’Amato, C., Esposito, F., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2010). Inductive reasoning and semantic web search. In S. Shin, S. Ossowski, M. Schumacher, M. J. Palakal, & C.-C. Hung (Eds.), Proceedings of the 2010 ACM Symposium on Applied Computing - SAC ’10. ACM. https://doi.org/10.1145/1774088.1774397 ( reposiTUm)

Cali, A., Gottlob, G., Kifer, M., Lukasiewicz, T., & Pieris, A. (2010). Ontological reasoning with F-Logic Lite and its extensions. In M. Fox & D. Poole (Eds.), Proceedings of the 24th National Conference on Artificial Intelligence (AAAI 2010) (pp. 1660–1665). AAAI Press. http://hdl.handle.net/20.500.12708/53559 ( reposiTUm)

d´Amato, C., Fanizzi, N., Fazzinga, B., Gottlob, G., & Lukasiewicz, T. (2009). Combining Semantic Web Search with the Power of Inductive Reasoning. In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009) (pp. 15–26). CEUR-Proceedings. http://hdl.handle.net/20.500.12708/53024 ( reposiTUm)

Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In V. De Antonellis, S. Castano, B. Catania, & G. Guerrini (Eds.), Proceedings of the 17th Italian Symposium on Advanced Database Systems (SEBD 2009) (pp. 29–36). Seneca Edizioni. http://hdl.handle.net/20.500.12708/53032 ( reposiTUm)

Lukasiewicz, T. (2009). Uncertainty in the Semantic Web. In L. Godo & A. Pugliese (Eds.), Scalable Uncertainty Management (pp. 2–11). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-04388-8_2 ( reposiTUm)

Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Tractable Query Answering over Ontologies with Datalog+-. In B. Cuenca Grau, I. Horrocks, B. Motik, & U. Sattler (Eds.), Proceedings of the 22nd International Workshop on Description Logics (DL 2009) (pp. 46:1-46:12). CEUR workshop proceedings. http://hdl.handle.net/20.500.12708/53039 ( reposiTUm)

Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In V. De Antonellis, S. Castano, B. Catania, & G. Guerrini (Eds.), Proceedings of the 17th Italian Symposium on Advanced Database Systems (SEBD 2009) (pp. 5–6). Seneca Edizioni. http://hdl.handle.net/20.500.12708/53031 ( reposiTUm)

Lukasiewicz, T. (2009). Uncertainty Reasoning for the Semantic Web. In A. F. Polleres & T. Swift (Eds.), Web Reasoning and Rule Systems (pp. 26–39). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-05082-4_3 ( reposiTUm)

Lukasiewicz, T., & Ragone, A. (2009). A Combination of Boolean Games with Description Logics for Automated Multi-Attribute Negotiation. In B. Cuenca Grau, I. Horrocks, B. Motik, & U. Sattler (Eds.), Proceedings of the 22nd International Workshop on Description Logics (DL 2009) (pp. 47:1-47:12). CEUR workshop proceedings. http://hdl.handle.net/20.500.12708/53040 ( reposiTUm)

Cali, A., Gottlob, G., & Lukasiewicz, T. (2009). Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In R. Fagin (Ed.), Proceedings of the 12th International Conference on Database Theory (ICDT 2009) (pp. 14–30). ACM International Conference Proceeding Series. http://hdl.handle.net/20.500.12708/53030 ( reposiTUm)

Calì, A., Gottlob, G., & Lukasiewicz, T. (2009). A general datalog-based framework for tractable query answering over ontologies. In J. Paredaens & S. Jianwen (Eds.), Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS ’09. ACM Press. https://doi.org/10.1145/1559795.1559809 ( reposiTUm)

Lukasiewicz, T., & Ragone, A. (2009). Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web. In R. Baeza-Yates, J. Lang, S. Mitra, S. Parsons, & G. Pasi (Eds.), Proceedings of the 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2009) (pp. 395–402). IEEE. http://hdl.handle.net/20.500.12708/53041 ( reposiTUm)

d´Amato, C., Fanizzi, N., Esposito, F., & Lukasiewicz, T. (2009). Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models. In R. Baeza-Yates, B. Berendt, E. Bertino, E.-P. Lim, & G. Pasi (Eds.), Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2009) (pp. 689–692). IEEE Computer Society. http://hdl.handle.net/20.500.12708/53042 ( reposiTUm)

d’Amato, C., Fanizzi, N., Esposito, F., & Lukasiewicz, T. (2009). Inductive Query Answering and Concept Retrieval Exploiting Local Models. In B. Lazzerini, L. Jain, A. Abraham, F. Marcelloni, F. Herrera, & V. Loia (Eds.), 2009 Ninth International Conference on Intelligent Systems Design and Applications. IEEE Computer Society. https://doi.org/10.1109/isda.2009.34 ( reposiTUm)

Lukasiewicz, T., & Ragone, A. (2008). Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web (SMRR). In R. Lara Hernandez, T. Di Noia, & I. Toma (Eds.), Proceedings of the 2nd International Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (SMRR 2008) (p. 15). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/52564 ( reposiTUm)

Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Representing Ontology Mappings with Probabilistic Description Logic Programs. In Proceedings of the 16th Italian Symposium on Advanced Database Systems (SEBD 2008) (pp. 438–445). http://hdl.handle.net/20.500.12708/52568 ( reposiTUm)

Cali, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Tightly Integrated Probabilistic Description Logic Programs for Representing Ontology Mappings. In S. Hartmann & G. Kern-Isberner (Eds.), Proceedings of the 5th International Symposium on Foundations of Information and Knowledge Systems (FoIKS 2008) (pp. 178–198). Springer LNCS. http://hdl.handle.net/20.500.12708/52570 ( reposiTUm)

d’Amato, C., Fanizzi, N., & Lukasiewicz, T. (2008). Tractable Reasoning with Bayesian Description Logics. In S. Greco & T. Lukasiewicz (Eds.), Scalable Uncertainty Management (pp. 146–159). Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/978-3-540-87993-0_13 ( reposiTUm)

Fanizzi, N., d´Amato, C., Esposito, F., & Lukasiewicz, T. (2008). Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations. In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, & T. Lukasiewicz (Eds.), Proceedings of the 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008) (p. 10). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/52565 ( reposiTUm)

Lukasiewicz, T. (2007). A Novel Combination of Answer Set Programming with Description Logics for the Semantic Web. In E. Franconi, M. Kifer, & W. May (Eds.), The Semantic Web: Research and Applications (pp. 384–398). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-72667-8_28 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web. In H. Prade & V. S. Subrahmanian (Eds.), Scalable Uncertainty Management (pp. 16–30). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-75410-7_2 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Tightly Integrated Fuzzy Description Logic Programs Under the Answer Set Semantics for the Semantic Web. In M. Marchiori, J. Z. Pan, & C. de Sainte Marie (Eds.), Web Reasoning and Rule Systems (pp. 289–298). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-72982-2_23 ( reposiTUm)

Lukasiewicz, T. (2007). Tractable Probabilistic Description Logic Programs. In H. Prade & V. S. Subrahmanian (Eds.), Scalable Uncertainty Management (pp. 143–156). Springer Lecture Notes in Artificial Intelligence. https://doi.org/10.1007/978-3-540-75410-7_11 ( reposiTUm)

Calì, A., & Lukasiewicz, T. (2007). Tightly Integrated Probabilistic Description Logic Programs for the Semantic Web. In V. Dahl & I. Niemelä (Eds.), Logic Programming (pp. 428–429). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-74610-2_30 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Description Logic Programs Under Probabilistic Uncertainty and Fuzzy Vagueness. In K. Mellouli (Ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty (pp. 187–198). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-75256-1_19 ( reposiTUm)

Farinelli, A., Finzi, A., & Lukasiewicz, T. (2007). Team Programming in Golog under Partial Observability. In M. Veloso (Ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) (pp. 2097–2102). AAAI Press/IJCAI. http://hdl.handle.net/20.500.12708/51959 ( reposiTUm)

Cali, A., Lukasiewicz, T., Prodoiu, L., & Stuckenschmidt, H. (2007). A Framework for Representing Ontology Mappings under Probabilities and Inconsistency. In F. Bobillo, P. Costa, C. d´Amato, N. Fanizzi, F. Fung, T. Lukasiewicz, & T. Martin (Eds.), Proceedings of the ISWC-2007 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2007). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51952 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2006). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies. In Y. Sure & J. Domingue (Eds.), Proceedings of the 3rd European Semantic Web Conference (ESWC 2006), Budva, Montenegro, June 2006 (pp. 288–302). Lecture Notes in Computer Science. Springer. http://hdl.handle.net/20.500.12708/51712 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2006). Adaptive Multi-Agent Programming in GTGolog. In C. Freksa, M. Kohlhase, & K. Schill (Eds.), Proceedings of the 29th Annual German Conference on Artificial Intelligence (KI 2006), Bremen, Germany, June 2006. (pp. 389–403). Lecture Notes in Computer Science. Springer. http://hdl.handle.net/20.500.12708/51714 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2006). Variable-Strength Conditional Preferences for Matchmaking in Description Logics. In P. Doherty, J. Mylopoulos, & C. Welty (Eds.), Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR 2006), Lake District, UK, June 2006. (pp. 164–174). AAAI Press. http://hdl.handle.net/20.500.12708/51713 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2006). Game-Theoretic Agent Programming in Golog under Partial Observability. In C. Freksa, M. Kohlhase, & K. Schill (Eds.), Proceedings of the 29th Annual German Conference on Artificial Intelligence (KI 2006), Bremen, Germany, June 2006. (pp. 113–127). Lecture Notes in Computer Science, Springer. http://hdl.handle.net/20.500.12708/51715 ( reposiTUm)

Lukasiewicz, T. (2006). Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web. In T. Eiter, E. Franconi, R. Hodgson, & S. Stephens (Eds.), Proceedings of the 2nd International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML 2006) (pp. 89–96). IEEE Computer Society. http://hdl.handle.net/20.500.12708/51717 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2006). Adaptive Multi-Agent Programming in GTGolog. In G. Brewka, S. Coradeschi, A. Perini, & P. Traverso (Eds.), Proceedings of the 17th biennial European Conference on Artificial Intelligence (ECAI 2006) (pp. 753–754). IOS Press. http://hdl.handle.net/20.500.12708/51716 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2006). Preferences, Links, and Probabilities for Ranking Objects in Ontologies. In P. C. G. da Costa, K. B. Laskey, K. J. Laskey, F. Fung, & M. Pool (Eds.), Proceedings of the ISWC-2006 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2006) (pp. 65–66). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51719 ( reposiTUm)

Cali, A., & Lukasiewicz, T. (2006). An Approach to Probabilistic Data Integration for the Semantic Web. In P. C. G. da Costa, K. B. Laskey, K. J. Laskey, F. Fung, & M. Pool (Eds.), Proceedings of the ISWC-2006 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2006) (pp. 67–68). CEUR Workshop Proceedings. http://hdl.handle.net/20.500.12708/51718 ( reposiTUm)

Lukasiewicz, T. (2005). Probabilistic Description Logic Programs. In L. Godo (Ed.), Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2005) (pp. 737–749). Springer. http://hdl.handle.net/20.500.12708/51339 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Golog under Partial Observability. In F. Dignum, V. Dignum, S. Koenig, & S. Kraus (Eds.), Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005) (pp. 1301–1302). ACM Press. http://hdl.handle.net/20.500.12708/51340 ( reposiTUm)

Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG. In Proceedings of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA 2005) (pp. 230–239). CMU. http://hdl.handle.net/20.500.12708/51341 ( reposiTUm)

Lukasiewicz, T. (2005). Stratified Probabilistic Description Logic Programs. In P. C. G. da Costa, K. B. Laskey, & K. J. Laskey (Eds.), Proceedings of the ISWC-2005 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2005) (pp. 87–97). http://hdl.handle.net/20.500.12708/51342 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Agent Programming in Golog under Partial Observability. In P. Gmytrasiewicz & S. Parsons (Eds.), Working Notes of IJCAI-05 Workshop on Game Theoretic and Decision Theoretic Agents (GTDT 2005). http://hdl.handle.net/20.500.12708/51343 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Reasoning About Actions in Nonmonotonic Causal Theories. In C. Baral, G. Greco, & N. Leone (Eds.), Proceedings of the 8th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2005) (pp. 185–197). Springer. http://hdl.handle.net/20.500.12708/51308 ( reposiTUm)

Cali, A., Gottlob, G., & Lukasiewicz, T. (2010). Datalog extensions for tractable query answering over ontologies. In R. De Virgilio, F. Giunchiglia, & L. Tanca (Eds.), Semantic Web Information Management: A Model-Based Perspective (pp. 249–279). Springer. http://hdl.handle.net/20.500.12708/27045 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2010). Tightly coupled fuzzy description logic programs under the answer set semantics for the Semantic Web. In M. Lytras & A. Sheth (Eds.), Progressive Concepts for Semantic Web Evolution: Applications and Developments (pp. 237–256). Information Science Reference. https://doi.org/10.4018/978-1-60566-992-2.ch011 ( reposiTUm)

Drabent, W., Eiter, T., Ianni, G., Krennwallner, T., Lukasiewicz, T., & Maluszynski, J. (2009). Hybrid Reasoning with Rules and Ontologies. In F. Bry & J. Maluszynski (Eds.), Semantic Techniques for the Web (pp. 1–49). Springer. https://doi.org/10.1007/978-3-642-04581-3_1 ( reposiTUm)

da Costa, P. C. G., Laskey, K. B., & Lukasiewicz, T. (2008). Uncertainty Representation and Reasoning in the Semantic Web. In J. Cardoso & M. Lytras (Eds.), Semantic Web Engineering in the Knowledge Society (pp. 315–340). Information Science Reference. http://hdl.handle.net/20.500.12708/26220 ( reposiTUm)

Calì, A., Lukasiewicz, T., Predoiu, L., & Stuckenschmidt, H. (2008). Rule-Based Approaches for Representing Probabilistic Ontology Mappings. In P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, & M. Pool (Eds.), Uncertainty Reasoning for the Semantic Web I (pp. 66–87). Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1_5 ( reposiTUm)

Calì, A., & Lukasiewicz, T. (2008). An Approach to Probabilistic Data Integration for the Semantic Web. In P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, & M. Pool (Eds.), Uncertainty Reasoning for the Semantic Web I (pp. 52–65). Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1_4 ( reposiTUm)

da Costa, P. C. G., d´Amato, C., Fanizzi, N., Laskey, K. B., Laskey, K. J., Lukasiewicz, T., Nickles, M., & Pool, M. (Eds.). (2008). Uncertainty Reasoning for the Semantic Web I : ISWC International Workshop, URSW 2005-2007, Revised Selected and Invited Papers. Springer LNCS. https://doi.org/10.1007/978-3-540-89765-1 ( reposiTUm)

Proceedings of the 1st International Workshop on Uncertainty in Description Logics (UniDL 2010). (2010). In T. Lukasiewicz, R. Penaloza, & A.-Y. Turhan (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23206 ( reposiTUm)

Proceedings of the 6th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2010). (2010). In F. Bobillo, R. Carvalho, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, & M. Pool (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23242 ( reposiTUm)

Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009). (2009). In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/23033 ( reposiTUm)

Proceedings of the 4th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2008). Volume 423 of CEUR Workshop Proceedings. (2008). In F. Bobillo, P. C. G. da Costa, C. d´Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, T. Martin, M. Nickles, M. Pool, & P. Smrz (Eds.), CEUR Workshop Proceedings. CEUR-WS.org. http://hdl.handle.net/20.500.12708/22812 ( reposiTUm)

Greco, S., & Lukasiewicz, T. (Eds.). (2008). Scalable Uncertainty Management. Springer, LNCS. https://doi.org/10.1007/978-3-540-87993-0 ( reposiTUm)

Proceedings of the ISWC-2007 Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2007). (2007). In F. Bobillo, P. Costa, N. Fanizzi, F. Fung, T. Lukasiewicz, T. Martin, M. Nickles, Y. Peng, M. Pool, P. Smrz, & P. Vojt (Eds.), CEUR Workshop Proceedings. CEUR-Proceedings. http://hdl.handle.net/20.500.12708/22349 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages. 4th European Semantic Web Conference (ESWC 2007), Innsbruck, Österreich, Austria. http://hdl.handle.net/20.500.12708/84626 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Tutorial on Managing Uncertainty and Vagueness in Semantic Web Languages. National Conference on Artificial Intelligence (AAAI), Pittsburgh, United States of America (the). http://hdl.handle.net/20.500.12708/84627 ( reposiTUm)

Eiter, T., Ianni, G., Lukasiewicz, T., & Schindlauer, R. (2009). Well-Founded Semantics for Description Logic Programs in the Semantic Web (INFSYS RR 1843-09-01). http://hdl.handle.net/20.500.12708/36178 ( reposiTUm)

Farinelli, A., Finzi, A., & Lukasiewicz, T. (2008). Team Programming in Golog under Partial Observability (INFSYS RR-1843-08-04). http://hdl.handle.net/20.500.12708/35356 ( reposiTUm)

Lukasiewicz, T., & Ragone, A. (2008). "Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web (INFSYS RR-1843-08-08). http://hdl.handle.net/20.500.12708/35360 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2008). Adaptive Game-Theoretic Agent Programming in Golog (INFSYS RR-1843-08-07). http://hdl.handle.net/20.500.12708/35359 ( reposiTUm)

Fazzinga, B., Gianforme, G., Gottlob, G., & Lukasiewicz, T. (2008). From Web Search to Semantic Web Search (INFSYS RR-1843-08-11). http://hdl.handle.net/20.500.12708/35362 ( reposiTUm)

Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2007). Combining Answer Set Programming with Description Logics for the Semantic Web (INFSYS RR-1843-07-04). http://hdl.handle.net/20.500.12708/33092 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Tightly Integrated Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web (INFSYS RR 1843-07-03). http://hdl.handle.net/20.500.12708/33094 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2007). Variable-Strength Conditional Preferences for Ranking Objects in Ontologies (INFSYS RR-1843-07-06). http://hdl.handle.net/20.500.12708/33093 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2007). Uncertainty and Vagueness in Description Logic Programs for the Semantic Web (INFSYS RR 1843-07-02). http://hdl.handle.net/20.500.12708/33096 ( reposiTUm)

Cali, A., & Lukasiewicz, T. (2007). Tightly Integrated Probabilistic Description Logic Programs. (INFSYS RR-1843-07-05). http://hdl.handle.net/20.500.12708/33095 ( reposiTUm)

Lukasiewicz, T., & Straccia, U. (2006). An Overview of Uncertainty and Vagueness in Description Logics for the Semantic Web (INFSYS RR-1843-06-07). http://hdl.handle.net/20.500.12708/33072 ( reposiTUm)

Lukasiewicz, T. (2006). A novel combination of answer set programming with description logics for the Semantic Web (INFSYS RR-1843-06-08). http://hdl.handle.net/20.500.12708/33073 ( reposiTUm)

Lukasiewicz, T. (2006). Probabilistic Description Logics for the Semantic Web (INFSYS RR-1843-06-05). http://hdl.handle.net/20.500.12708/33071 ( reposiTUm)

Lukasiewicz, T. (2006). Probabilistic Description Logic Programs (INFSYS RR-1843-06-04). http://hdl.handle.net/20.500.12708/33070 ( reposiTUm)

Lukasiewicz, T., & Schellhase, J. (2005). Variable-Strength Conditional Preferences for Matchmaking in Description Logics. http://hdl.handle.net/20.500.12708/33053 ( reposiTUm)

Lukasiewicz, T. (2005). Nonmonotonic Probabilistic Logics under Variable-Strength Inheritance with Overriding: Algorithms and Implementation in NMPROBLOG. http://hdl.handle.net/20.500.12708/33047 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Reasoning about Actions in Nonmonotonic Causal Theories. http://hdl.handle.net/20.500.12708/33048 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2005). Game-Theoretic Golog under Partial Observability. http://hdl.handle.net/20.500.12708/33046 ( reposiTUm)

Finzi, A., & Lukasiewicz, T. (2004). Game-Theoretic Agent Programming in Golog. http://hdl.handle.net/20.500.12708/32946 ( reposiTUm)

Eiter, T., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2003). Combining Answer Set Programming with Description Logics for the Semantic Web. http://hdl.handle.net/20.500.12708/32933 ( reposiTUm)

Eiter, T., & Lukasiewicz, T. (2002). Causes and Explanations in the Structural-Model Approach: Tractable Cases (INFSYS RR-1843-02-03). http://hdl.handle.net/20.500.12708/32793 ( reposiTUm)

Lukasiewicz, T. (2001). Databases and logic programming under probabilistic uncertainty [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/179030 ( reposiTUm)