March 2015The most familiar relations in #skos, skos:broader, skos:narrower & skos:related correspond to familiar thesaurus relations #semanticweb
ift.tt the results are richer and more precise, as compared to today's mainstream search engines. We accomplish this because relevance is judged not only on the basis of plain string frequencies, but also taking into account relationships between concepts and entities in the triplestore. Je pensais avoir évité le problème de la bombe de la guerre à Louvain mais tous les trains sont à la vitese de l'escargot à Bruxelles à cause de personnes sur la voie. A Bruxelles midi je descends pour continuer à vélo mais au micro ils annoncent que le train va repartir. Alors je remonte dans le train. Ils ferment les portes et on reste 20 minutes enfermés à la gare avant de partir au pas vers Bruxelles Nord. Quand ca veut pas ça veut pas...
RT @bernardpivot1: "Il y a quatre types idéals: le crétin, l'imbécile, le stupide et le fou. Le normal, c'est le mélange équilibré des quatre." Umberto Eco
ift.tt RT @bernardpivot1: "Il y a quatre types idéals: le crétin, l'imbécile, le stupide et le fou. Le normal, c'est le mélange équilibré des quatre." Umberto Eco
ift.tt @greefhorst Thanks ! will use it when people say architecture work is not necessary because "we do agile". #entarch #agile #togaf
ift.tt “I choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it”. It would be a quote by Bill Gates but apparently it isn’t: http://quoteinvestigator.com/2014/02/26/lazy-job/ I found it in this presentation :
SQL Server Days 2013 – Create ETL solutions faster with metadata driven development from KoenVerbeeck
RT @marcellovena: Innovation? No thanks, we are too busy... @posth @arhomberg http://t.co/Pfs05Y83v1
http://ift.tt/1IjtKNj Faut pas se laisser faire...
Sortie route blancs gilets très sympa ce matin avec série de sprints pour finir pour Thomas Declercq et Guillaume.
Caroline Declercq on voit ta maison
“I initially thought that AI and machine learning would be great for augmenting the productivity of human quants. One of the things human quants do, that machine learning doesn’t do, is to understand what goes into a model and to make sense of it. That’s important for convincing managers to act on analytical insights. For example, an early analytics insight at Osco Pharmacy uncovered that people who bought beer also bought diapers. But because this insight was counter-intuitive and discovered by a machine, they didn’t do anything with it. But now companies have needs for greater productivity than human quants can address or fathom. They have models with 50,000 variables. These systems are moving from augmenting humans to automating decisions.” https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business?utm_content=buffer07b27&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer |