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Reasoning Web. Learning, Uncertainty, Streaming, and Scalability [electronic resource] :14th International Summer School 2018, Esch-sur-Alzette, Luxembourg, September 22–26, 2018, Tutorial Lectures /

Contributor(s): d’Amato, Claudia [editor.] | Theobald, Martin [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Information Systems and Applications, incl. Internet/Web, and HCI: 11078Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XI, 237 p. 47 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030003388.Subject(s): Computer Science | Database management | Data mining | Artificial intelligence | Information systems | Computer science | Database Management | Data Mining and Knowledge Discovery | Artificial Intelligence | Computer Appl. in Administrative Data Processing | Mathematical Logic and Formal LanguagesOnline resources: Click here to access online
Contents:
Practical 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. .
In: Springer eBooksSummary: The 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.
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Practical 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. .

The 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.

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