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. .. , 23 4.1 Diffusion of events to different language communities in Wikipedia; the clock symbol denotes the real world occurrence of an event, p.42

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. .. , Entity-level (semantic) graph G sem at snapshot t, vol.46

, Conceptual approach behind the linked-based prediction methods, p.48

, US-Mexico diplomatic crisis in 2017". (Graphical elements via Wikimedia Commons), Conceptual approach of the ELEVATE pipeline illustrating the event on the

, Multi-label classifier for event spread prediction using one-against-all problem transformation, i.e., a set of classifiers select the languages to be included in event spread

, Taxonomy of different kinds of events from the perspective of societal relevance

, Graphical representation of the evolution of event parameters, p.58

, An illustration on aligning Web contents onto a fine-grained type hierarchy 68

, 2 An example on a small fragment of our type hierarchy illustrating the computation of a type score vector for an entity

, An illustration of the computation of semantic fingerprint for a document, p.71

, Conceptual approach by the example of a document of type club, p.73

.. .. Full,

. .. , 78 6.2 Conceptual approach of the ELEVATE-live pipeline illustrated by a Brexit related news article

, An illustration depicting the exploration of news article virality with the help of ELEVATE-live

, Country-specific viral/relevant news assessment

, An RDF graph example -RDF triples for resource Albert_Einstein in DBpedia; dbr and dbo, stand for DBpedia resource, and ontology/schema, respectively

, An RDFS definition example -RDF triples specifying a segment of DBpedia ontology/schema

. .. , Taxonomy of various event detection techniques, p.27

. .. , 31 3.3 A comparative depiction of various type classification works on entity and document levels

, YAGO relations used for entity-level analytics

, Temporal evolution of prediction parameters

, Macro-average scores for the adjusted threshold based models after 5 days (#PC: number of predictions)

, Macro-average scores for the adjusted threshold based models after 10 days (#PC: number of predictions)

, Macro-average scores for the adjusted threshold based models after 20 days (#PC: number of predictions)

, Micro-average scores for the adjusted threshold based models after 5 days (#PC: number of predictions)

, Micro-average scores for the adjusted threshold based models after 10 days (#PC: number of predictions), p.61

, Micro-average scores for the adjusted threshold based models after 20 days (#PC: number of predictions), p.61

, Macro-average scores for the machine learning approach after 5 days (#PC: number of predictions)

, Macro-average scores for the machine learning approach after 10 days (#PC: number of predictions)

, Macro-average scores for the machine learning approach after 20 days (#PC: number of predictions)

, Micro-average scores for the machine learning approach after 5 days (#PC: number of predictions)

, Micro-average scores for the machine learning approach after 10 days (#PC: number of predictions)

, Micro-average scores for the machine learning approach after 20 days (#PC: number of predictions)

, Number of predicted languages per method

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. .. , Micro-average scores for document type classification, p.75

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