http://www.ronpub.com/publications/search.php?journal=ALL&author=Nuno+Silva&exactauthor=on&title=&abstract=&volume=&issue=&year1=&year2=&searchtype=advanced This feed contains the result of an user-defined search in RonPub publications en-us https://www.ronpub.com/ojsw/OJSW_2016v3i1n01_Peixoto.html http://nbn-resolving.de/urn:nbn:de:101:1-201705194907 Extracting valuable data among large volumes of data is one of the main challenges in Big Data. In this paper, a Hierarchical Multi-Label Classification process called Semantic HMC is presented. This process aims to extract valuable data from very large data sources, by automatically learning a label hierarchy and classifying data items.The Semantic HMC process is composed of five scalable steps, namely Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically a label hierarchy from statistical analysis of data. This paper focuses on the last two steps which perform item classification according to the label hierarchy. The process is implemented as a scalable and distributed application, and deployed on a Big Data platform. A quality evaluation is described, which compares the approach with multi-label classification algorithms from the state of the art dedicated to the same goal. The Semantic HMC approach outperforms state of the art approaches in some areas.