Feng, R., Acar, E., Wang, Y., Schlobach, S., Liu, W., & Ding, W. (2022). Computing Sufficient and Necessary Conditions in CTL: A Forgetting Approach. Information Sciences, 616, 474-504. https://doi.org/10.1016/j.ins.2022.10.124[details]
Ho, L., Acar, E., Arch-int, , S., Schlobach, K. S., & Arch-int, N. (2022). An argumentative approach for handling inconsistency in prioritized Datalog± ontologies. AI Communications.
Kalo, J-C., Kruit, B., & Schlobach, S. (2022). Understanding Distantly Supervised Relation Extraction through Semantic Error Analysis. In 4th Conference on Automated Knowledge Base Construction
2020
Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587[details]
van Wierst, P., Vrijenhoek, S., Schlobach, S., & Betti, A. (2016). Phil@Scale: Computational Methods within Philosophy. In L. Wieneke, C. Jones, M. Düring, F. Armaselu, & R. Leboutte (Eds.), Proceedings of the Third Conference on Digital Humanities in Luxembourg with a Special Focus on Reading Historical Sources in the Digital Age: Luxembourg, Luxembourg, December 5-6, 2013 (CEUR Workshop Proceedings; Vol. 1681). CEUR-WS. http://ceur-ws.org/Vol-1681/Wierst_et_al_Philo_at_scale.pdf[details]
Hoekstra, R., Magliacane, S., Rietveld, L., de Vries, G., Wibisono, A., & Schlobach, S. (2015). Hubble: Linked Data Hub for Clinical Decision Support. In E. Simperl, B. Norton, D. Mladenic, E. Della Valle, I. Fundulaki, A. Passant, & R. Troncy (Eds.), The Semantic Web: ESWC 2012 Satellite Events: ESWC 2012 Satellite Events, Heraklion, Crete, Greece, May 27-31, 2012 : revised selected papers (pp. 458-462). (Lecture Notes in Computer Science; Vol. 7540). Springer. https://doi.org/10.1007/978-3-662-46641-4_45[details]
2014
Beek, W., Groth, P., Schlobach, S., & Hoekstra, R. (2014). A Web Observatory for the Machine Processability of Structured Data on the Web. In WebSci'14: proceedings of the 2014 ACM Web Science Conference: June 23-26, 2014, Bloomington, IN, USA (pp. 249-250). Association for Computing Machinery. https://doi.org/10.1145/2615569.2615654[details]
Rietveld, L., Hoekstra, R., Schlobach, S., & Guéret, C. (2014). Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. In P. Mika, T. Tudorache, A. Bernstein, C. Welty, C. Knoblock, D. Vrandečić, P. Groth, N. Noy, K. Janowicz, & C. Goble (Eds.), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014: proceedings (Vol. 2, pp. 81-96). (Lecture Notes in Computer Science; Vol. 8797). Springer. https://doi.org/10.1007/978-3-319-11915-1_6[details]
2013
Meroño-Peñuela, A., Guéret, C., Hoekstra, R., & Schlobach, S. (2013). Detecting and Reporting Extensional Concept Drift in Statistical Linked Data. In S. Capadisli, F. Cotton, R. Cyganiak, A. Haller, A. Hamilton, & R. Troncy (Eds.), Proceedings of the 1st International Workshop on Semantic Statistics: co-located with 12th International Semantic Web Conference (ISWC 2013) : Sydney, Australia, October 11th, 2013 Article 10 (CEUR Workshop Proceedings; Vol. 1549). CEUR-WS. http://ceur-ws.org/Vol-1549/article-10.pdf[details]
Meroño-Peñuela, A., Ashkpour, A., Rietveld, L., Hoekstra, R., & Schlobach, S. (2012). Linked Humanities Data: The Next Frontier? A Case-study in Historical Census Data. In T. Kauppinen, L. C. Pouchard, & C. Keßler (Eds.), Proceedings of the Second International Workshop on Linked Science 2012 - Tackling Big Data: in conjunction with the International Semantic Web Conference (ISWC2012) : Boston, MA, USA, November 12, 2012 Article 3 (CEUR Workshop Proceedings; Vol. 951). CEUR-WS. http://ceur-ws.org/Vol-951/paper3.pdf[details]
Wang, S., Englebienne, G., Gueret, C., Schlobach, S., Isaac, A., & Schut, M. (2010). Similarity features, and their role in concept alignment learning. In M. Popescu, & D. L. Stewart (Eds.), SEMAPRO 2010: the Fourth International Conference on Advances in Semantic Processing: October 25-30, 2010, Florence, Italy (pp. 1-6). IARIA. http://www.thinkmind.org/index.php?view=article&articleid=semapro_2010_1_10_50023[details]
2008
Wang, S., Englebienne, G., & Schlobach, S. (2008). Learning concept mappings from instance similarity. In A. Sheth, S. Staab, M. Dean, M. Paolucci, D. Maynard, T. Finin, & K. Thirunarayan (Eds.), The Semantic Web - ISWC 2008: 7th International Semantic Web Conference, ISWC 2008, Karlsruhe, Germany, October 26-30, 2008 : proceedings (pp. 339-355). (Lecture Notes in Computer Science; Vol. 5318). Springer. https://doi.org/10.1007/978-3-540-88564-1_22[details]
2018
van der Meulen, A., Kwisthout, J., ten Teije, A., Schlobach, S., van Splunter, S., Winands, M., van Netten, S., Visser, A., van Someren, M., Dastani, M., & Dignum, F. (2018). Frame of Reference - Bachelor’s and Master’s Programmes in Artificial Intelligence: The Dutch Perspective. Kunstmatige Intelligentie Opleidingen Nederland (KION). [details]
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