Knowledge in the world continuously evolves, and ontologies are largely incomplete.
We propose a method and a tool for discovering emerging entities by extracting them from social media.
Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we propose a mixed syntactic + semantic method.
I’d like to report on our demonstration paper at WWW 2017, focusing on Spark-based Big Data Analysis of Semantic IFML Models and Web Logs for Enhanced User Behavior Analytics. The motivation of the work is that no approaches exist for merging web log analysis and statistics with information about the Web application structure, content and semantics. Indeed, … Continue reading Spark-based Big Data Analysis of Semantic IFML Models and Web Logs for Enhanced User Behavior Analytics
We study spreading of social content in space during live events, measuring the spreading of the event propagation in space. We build didifferent clusters of fashion brands, we characterize several features of propagation in space and we correlate them to the popularity of the brand and temporal propagation.
On March 10, 2017 we hosted a seminar by Daniele Quercia in the Como Campus of Politecnico di Milano, on the topic: Good City Life Daniele Quercia leads the Social Dynamics group at Bell Labs in Cambridge (UK). He has been named one of Fortune magazine’s 2014 Data All-Stars, and spoke about “happy maps” at TED. … Continue reading Data Science for Good City Life