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024 7 _a10.1007/978-3-030-00338-8
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245 1 0 _aReasoning Web. Learning, Uncertainty, Streaming, and Scalability
_h[electronic resource] :
_b14th International Summer School 2018, Esch-sur-Alzette, Luxembourg, September 22-26, 2018, Tutorial Lectures /
_cedited by Claudia d'Amato, Martin Theobald.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXI, 237 p. 47 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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347 _atext file
_bPDF
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490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v11078
505 0 _aPractical Normative Reasoning with Defeasible Deontic Logic -- Efficient SPARQL Queries on Very Large Knowledge Graphs -- A Tutorial on Query Answering and Reasoning over Probabilistic Knowledge Bases -- Cold-start Knowledge Base Population using Ontology-based Information Extraction with Conditional Random Fields -- Machine Learning with and for Knowledge Graphs -- Rule Induction and Reasoning over Knowledge Graphs -- Storing and Querying Semantic Data in the Cloud -- Engineering of Web Stream Processing Applications -- Reasoning at Scale. .
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.
_992523
650 1 4 _aDatabase Management.
_93157
650 2 4 _aData Mining and Knowledge Discovery.
_992524
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Application in Administrative Data Processing.
_931588
650 2 4 _aFormal Languages and Automata Theory.
_992525
700 1 _ad'Amato, Claudia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_992526
700 1 _aTheobald, Martin.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_992527
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030003371
776 0 8 _iPrinted edition:
_z9783030003395
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v11078
_992529
856 4 0 _uhttps://doi.org/10.1007/978-3-030-00338-8
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