000			02170nam a2200265Ia 4500
000			03009nam a22002895i 4500
001			978-3-031-31414-8
003			DE-He213
005			20240319121157.0
007			cr nn 008mamaa
008			230427s2023 sz | s |||| 0|eng d
020			_a9783031314148 _9978-3-031-31414-8
082			_a5.3
245			_aReasoning Web. Causality, Explanations and Declarative Knowledge _cedited by Leopoldo Bertossi, Guohui Xiao. _h[electronic resource] :
250			_a1st ed. 2023.
260			_aCham _bSpringer Nature Switzerland _c2023
300			_aIX, 211 p. 22 illus., 15 illus. in color. _bonline resource.
520			_aThe purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers. The broad theme of this year's summer school was "Reasoning in Probabilistic Models and Machine Learning" and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.
650			_aApplication software. _940338
650			_aComputer and Information Systems Applications. _940339
700			_aBertossi, Leopoldo. _940340
700			_aXiao, Guohui. _940341
856			_uhttps://doi.org/10.1007/978-3-031-31414-8
942			_cEBK _2ddc
999			_c15939 _d15939