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020 _a9783030954819
_9978-3-030-95481-9
024 7 _a10.1007/978-3-030-95481-9
_2doi
050 4 _aQA76.76.A65
072 7 _aUB
_2bicssc
072 7 _aCOM005000
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245 1 0 _aReasoning Web. Declarative Artificial Intelligence
_h[electronic resource] :
_b17th International Summer School 2021, Leuven, Belgium, September 8-15, 2021, Tutorial Lectures /
_cedited by Mantas Šimkus, Ivan Varzinczak.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aIX, 185 p. 33 illus., 9 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 ;
_v13100
505 0 _aFoundations of Graph Path Query Languages -- On Combining Ontologies and Rules -- Modelling Symbolic Knowledge using Neural Representations -- Mining the Semantic Web with Machine Learning: main issues that need to be known -- Temporal ASP: from logical foundations to practical use with telingo -- A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs -- Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis.
520 _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 again "Declarative Artificial Intelligence" 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: Foundations of Graph Path Query Languages; On Combining Ontologies and Rules; Modelling Symbolic Knowledge Using Neural Representations; Mining the Semantic Web with Machine Learning: Main Issues That Need to Be Known; Temporal ASP: From Logical Foundations to Practical Use with telingo; A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs; and Score-Based Explanations in Data Management and Machine Learning.
650 0 _aApplication software.
_9124153
650 0 _aArtificial intelligence.
_93407
650 0 _aExpert systems (Computer science).
_93392
650 0 _aMathematical logic.
_92258
650 1 4 _aComputer and Information Systems Applications.
_9124154
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aKnowledge Based Systems.
_979172
650 2 4 _aMathematical Logic and Foundations.
_934712
700 1 _aŠimkus, Mantas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9124155
700 1 _aVarzinczak, Ivan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9124156
710 2 _aSpringerLink (Online service)
_9124157
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030954802
776 0 8 _iPrinted edition:
_z9783030954826
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v13100
_9124158
856 4 0 _uhttps://doi.org/10.1007/978-3-030-95481-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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