Reasoning Web. Learning, Uncertainty, Streaming, and Scalability

Autor: Claudia D'Amato, Martin Theobald

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer Nature Switzerland AG

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This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-s...

Informácie o knihe

Autor

Claudia D'Amato, Martin Theobald

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2018

Stránok

237

EAN

9783030003371

ISBN

303000337X

Enbook ID

20085649

Vydavateľ

Springer Nature Switzerland AG

Hmotnosť

391

Rozmery

155 x 235 x 12

Kompletný popis

This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-sur-Alzette, Luxembourg, in September 2018. 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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Reasoning Web. Learning, Uncertainty, Streaming, and Scalability

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