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020 _a9783030314231
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024 7 _a10.1007/978-3-030-31423-1
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
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072 7 _aCOM021000
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082 0 4 _a005.74
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245 1 0 _aReasoning Web. Explainable Artificial Intelligence
_h[electronic resource] :
_b15th International Summer School 2019, Bolzano, Italy, September 20-24, 2019, Tutorial Lectures /
_cedited by Markus Krötzsch, Daria Stepanova.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXI, 283 p. 366 illus., 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v11810
505 0 _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.
520 _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 shownuseful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
650 0 _aDatabase management.
_93157
650 0 _aData mining.
_93907
650 0 _aArtificial intelligence.
_93407
650 0 _aInformation technology
_xManagement.
_95368
650 0 _aMachine theory.
_998840
650 1 4 _aDatabase Management.
_93157
650 2 4 _aData Mining and Knowledge Discovery.
_998843
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Application in Administrative Data Processing.
_931588
650 2 4 _aFormal Languages and Automata Theory.
_998845
700 1 _aKrötzsch, Markus.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998846
700 1 _aStepanova, Daria.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998849
710 2 _aSpringerLink (Online service)
_998850
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030314224
776 0 8 _iPrinted edition:
_z9783030314248
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v11810
_998851
856 4 0 _uhttps://doi.org/10.1007/978-3-030-31423-1
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