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035   $a(DE-He213)978-3-030-31423-1
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100   $a20190917d2019      u|             0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aReasoning Web. Explainable Artificial Intelligence$b[electronic resource] $e15th International Summer School 2019, Bolzano, Italy, September 20?24, 2019, Tutorial Lectures /$fedited by Markus Krötzsch, Daria Stepanova
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (XI, 283 p. 366 illus., 23 illus. in color.) 
225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v11810
311   $a3-030-31422-7 
327   $aClassical Algorithms for Reasoning and Explanation in Description Logics -- Explanation-Friendly Query Answering Under Uncertainty -- Provenance in Databases: Principles and Applications -- Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases -- Explaining Data with Formal Concept Analysis -- Logic-based Learning of Answer Set Programs -- Constraint Learning: An Appetizer -- A Modest Markov Automata Tutorial -- Explainable AI Planning (XAIP): Overview and the Case of Contrastive.
330   $aThe research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
410  0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v11810
606   $aDatabase management
606   $aData mining
606   $aArtificial intelligence
606   $aApplication software
606   $aMathematical logic
606   $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComputer Appl. in Administrative Data Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/I2301X
606   $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048
615  0$aDatabase management.
615  0$aData mining.
615  0$aArtificial intelligence.
615  0$aApplication software.
615  0$aMathematical logic.
615 14$aDatabase Management.
615 24$aData Mining and Knowledge Discovery.
615 24$aArtificial Intelligence.
615 24$aComputer Appl. in Administrative Data Processing.
615 24$aMathematical Logic and Formal Languages.
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702   $aKrötzsch$b Markus$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aStepanova$b Daria$4edt$4http://id.loc.gov/vocabulary/relators/edt
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906   $aBOOK
912   $a996466421903316
996   $aReasoning Web. Explainable Artificial Intelligence$92535219
997   $aUNISA