LEADER 03597nam 2200517 450 001 996525670603316 005 20230614053023.0 010 $a9783031314148$b(electronic bk.) 010 $z9783031314131 024 7 $a10.1007/978-3-031-31414-8 035 $a(MiAaPQ)EBC7242362 035 $a(Au-PeEL)EBL7242362 035 $a(DE-He213)978-3-031-31414-8 035 $a(OCoLC)1378065137 035 $a(PPN)269655433 035 $a(EXLCZ)9926529027100041 100 $a20230614d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aReasoning web. causality, explanations and declarative knowledge $e18th international summer school 2022, Berlin, Germany, September 27-30, 2022, tutorial lectures /$fLeopoldo Bertossi and Guohui Xiao, editors 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer, Springer Nature Switzerland AG,$d[2023] 210 4$dİ2023 215 $a1 online resource (219 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13759 311 08$aPrint version: Bertossi, Leopoldo Reasoning Web. Causality, Explanations and Declarative Knowledge Cham : Springer,c2023 9783031314131 320 $aIncludes bibliographical references and index. 327 $aExplainability 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. 330 $aThe purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers. The broad theme of this year's summer school was ?Reasoning in Probabilistic Models and Machine Learning? and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13759 606 $aArtificial intelligence$vCongresses 606 $aSemantic Web$vCongresses 615 0$aArtificial intelligence 615 0$aSemantic Web 676 $a025.0427 702 $aBertossi$b Leopoldo 702 $aXiao$b Guohui 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996525670603316 996 $aReasoning web. causality, explanations and declarative knowledge$93391546 997 $aUNISA