TU Wien Informatics

20 Years

Roles

2024

2023

2022

  • BECEL: Benchmark for Consistency Evaluation of Language Models / 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.
  • Clustering Generative Adversarial Networks for Story Visualization / 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
  • Explaining Chest X-Ray Pathologies in Natural Language / 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
  • NoiER: An Approach for Training more Reliable Fine-Tuned Downstream Task Models / 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
  • Memory-Driven Text-to-Image Generation / 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).
  • Image-to-Image Translation with Text Guidance / 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).
  • Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction / 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
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  • Learning to Model Multimodal Semantic Alignment for Story Visualization / 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
    Download: PDF (2.68 MB)
  • Democratizing Financial Knowledge Graph Construction by Mining Massive Brokerage Research Reports / 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.
  • Predictive Coding beyond Gaussian Distributions / 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).
  • Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence / 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
  • NP-Match: When Neural Processes meet Semi-Supervised Learning / 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.
  • Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup / 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
  • Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models / 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).
  • Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? / 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
  • Learning on Arbitrary Graph Topologies via Predictive Coding / 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.
  • Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations / 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.
  • Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics / 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
  • Deep Learning with Logical Constraints / 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
  • (Non-)Convergence Results for Predictive Coding Networks / 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

2016

  • Generalized Consistent Query Answering under Existential Rules / 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

2015

  • From Classical to Consistent Query Answering under Existential Rules / 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
    Project: START (2014–2022)
  • From Classical to Consistent Query Answering under Existential Rules / 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. http://hdl.handle.net/20.500.12708/56408
    Project: START (2014–2022)

2012

2011

2010

  • Datalog extensions for tractable query answering over ontologies / 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
  • Redundancy Elimination on RDF Graphs in the Presence of Rules, Constraints, and Queries / 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
  • Semantic search on the Web / 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
  • Semantic Web search based on ontological conjunctive queries / 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
  • Combining Semantic Web search with the power of inductive reasoning / 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
  • A novel combination of answer set programming with description logics for the Semantic Web / 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
  • Ontological reasoning with F-Logic Lite and its extensions / 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
  • Inductive reasoning and semantic web search / 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
  • Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications / 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
  • Proceedings of the 6th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2010) / 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
  • Proceedings of the 1st International Workshop on Uncertainty in Description Logics (UniDL 2010) / 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

2009

  • Hybrid Reasoning with Rules and Ontologies / 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
    Projects: HEX-Programme (2008–2012) / Hybride Wissensbasen (2008–2012) / IncMan (2009–2012)
  • Uncertainty Reasoning for the Semantic Web / 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
  • Reasoning about Actions with Sensing under Qualitative and Probabilistic Uncertainty / 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
  • Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web / 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
  • Description Logic Programs under Probabilistic Uncertainty and Fuzzy Vagueness / 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
  • Combining Semantic Web Search with the Power of Inductive Reasoning / 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
  • Inductive Query Answering and Concept Retrieval Exploiting Local Models / 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
  • Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models / 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
  • Combining Boolean Games with the Power of Ontologies for Automated Multi-Attribute Negotiation in the Semantic Web / 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
  • A Combination of Boolean Games with Description Logics for Automated Multi-Attribute Negotiation / 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
  • Tractable Query Answering over Ontologies with Datalog+- / 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
  • Uncertainty in the Semantic Web / Lukasiewicz, T. (2009). Uncertainty in the Semantic Web. In L. Godo & A. Pugliese (Eds.), Lecture Notes in Computer Science (pp. 2–11). Springer Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-04388-8_2
  • A general datalog-based framework for tractable query answering over ontologies / 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
  • A General Datalog-Based Framework for Tractable Query Answering over Ontologies / 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
  • Datalog±: A Unified Approach to Ontologies and Integrity Constraints / 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
  • Datalog±: A Unified Approach to Ontologies and Integrity Constraints / 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
  • Well-Founded Semantics for Description Logic Programs in the Semantic Web / 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
    Projects: HEX-Programme (2008–2012) / Hybride Wissensbasen (2008–2012)
  • Proceedings of the Fifth International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009) / 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

2008

2007

2006

  • Causes and Explanations in the Structural-Model Approach: Tractable Cases / 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
  • An Approach to Probabilistic Data Integration for the Semantic Web / 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
  • Variable-Strength Conditional Preferences for Ranking Objects in Ontologies / 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
  • Preferences, Links, and Probabilities for Ranking Objects in Ontologies / 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
  • Fuzzy Description Logic Programs under the Answer Set Semantics for the Semantic Web / 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
  • Adaptive Multi-Agent Programming in GTGolog / 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
  • Game-Theoretic Agent Programming in Golog under Partial Observability / 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
  • Adaptive Multi-Agent Programming in GTGolog / 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
  • Variable-Strength Conditional Preferences for Matchmaking in Description Logics / 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
  • A novel combination of answer set programming with description logics for the Semantic Web / 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
  • An Overview of Uncertainty and Vagueness in Description Logics for the Semantic Web / 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
  • Probabilistic Description Logics for the Semantic Web / Lukasiewicz, T. (2006). Probabilistic Description Logics for the Semantic Web (INFSYS RR-1843-06-05). http://hdl.handle.net/20.500.12708/33071
  • Probabilistic Description Logic Programs / Lukasiewicz, T. (2006). Probabilistic Description Logic Programs (INFSYS RR-1843-06-04). http://hdl.handle.net/20.500.12708/33070

2005

2004

2003

2002

 

  • Deep learning für das Semantic Web / Hohenecker, P. (2016). Deep learning für das Semantic Web [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.37489
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