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020 _a9783030003388
_9978-3-030-00338-8
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082 0 4 _a005.74
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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
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI ;
_v11078
500 _aAcceso multiusuario
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 shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aApplication software.
650 0 _aMathematical logic.
650 1 4 _aDatabase Management.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18024
650 2 4 _aData Mining and Knowledge Discovery.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aComputer Appl. in Administrative Data Processing.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I2301X
650 2 4 _aMathematical Logic and Formal Languages.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I16048
700 1 _ad'Amato, Claudia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTheobald, Martin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
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 ;
_v11078
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-030-00338-8
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