http://www.ronpub.com/publications/search.php?journal=ALL&author=Christophe+Cruz&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. https://www.ronpub.com/ojwt/OJWT_2018v5i1n04_Cruz.html http://nbn-resolving.de/urn:nbn:de:101:1-2018093019302313586232 This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the twocommunities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, byapproach the similarity between words or by revealing hidden semantic relations. Thus, these controlledvocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process. https://www.ronpub.com/ojwt/OJWT_2018v5i1n01e_WDRAR2018.html http://nbn-resolving.de/urn:nbn:de:101:1-2018110508370722588082 The first Web Data Processing & Reasoning (WDPAR) workshop in conjunction with the 41st German Conference on Artificial Intelligence (KI) taking place in Berlin, Germany in 2018 is a forum for all researchers especially interested in processing of and reasoning on web data. The proceedings of WDPAR@KI 2018 are published in the Open Journal of Web Technologies (OJWT) (www.ronpub.com/ojwt) as special issue and the publisher of OJWT is RonPub. This editorial provides an overview over the aims and scope of the workshop and the review procedure. Furthermore, we introduce the accepted papers and their topics in the editorial.