When the amount of RDF data is very large, it becomes more likely that the triples describing entities will contain errors and may not include the specification of a class from a known ontology. The work presented here explores the utilization of methods from machine learning to develop classifiers for identifying the semantic categorization of entities based upon the property names used to describe the entity. The goal is to develop classifiers that are accurate, but robust to errors and noise. The training data comes from DBpedia, where entities are categorized by type and densely described with RDF properties. The initial experimentation reported here indicates that the approach is promising.
This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.
Last week, machine learning took a big leap forward when Google's AlphaGo, a machine algorithm, beat the world champion, Lee Sedol, in the game Go. If the lip-reading technology had been used during the 2006 World Cup Final, when Zinedine Zidane was given a red card for headbutting Marco Materazzi, the outcome of the game could have been different. The partnership will provide students within the university's Department of Computing Science the opportunity to gain hands-on experience of IBM's … A new study reveals that voice assistant AIs, like Siri and Cortana, might be clever, but they lack fundamental empathy at their core. Google has entered into the machine learning market with the alpha release of Cloud Machine Learning. "Extraordinary" merger of machine intelligence and cloud economics, is changing business operations and society, says Leading Edge Forum.
During the British summer, conversations about sport become almost ubiquitous. This year, however, one participant in those conversations was very different: IBM Watson, IBM's cognitive intelligence. The All England Lawn Tennis Club knew that 2016 would feature unusually fierce competition for attention, with the Tour de France and Euro 2016 taking place alongside Wimbledon. More than ever before, social media was going to be a vital tool in directing that conversation, and directing attention to SW19. Wimbledon's "Cognitive Command Centre" – powered by Watson's intelligence running on a hybrid, IBM-managed cloud - scanned social media for emerging news and trends.