Over the last two decades, the amount of data which has been created, published and managed using Semantic Web standards and especially via Resource Description Framework (RDF) has been increasing.As a result, efficient processing of such big RDF datasets has become challenging.Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size.In this study, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets using a semantic-based partition and is implemented inside the state-of-the
PhD Student / Research Associate
University of Bonn
https://www.informatik.uni-bonn.de/de
PhD Student & Research Associate at the University of Bonn, Smart Data Analytics (SDA). My research interest are in the area of Semantic Web, Big Data and Machine Learning. I am also interested in the area of distributed computing systems (Apache Spark, Apache Flink).
One of the key features of a successful online employment marketplace is the ability to match people with the most relevant job opportunities. Our business uses data about candidates, jobs and hirers to perform this task. One valuable data point in this process is the job titles, which we discover in semi-structured forms in a candidate’s employment history and in a hirer’s job advertisement.
Head of Ontology
SEEK Limited
https://www.seek.com.au/
Andrew Young is Head of Ontology at SEEK Group, a diverse group of companies encompassing a strong portfolio of employment, education and volunteer businesses across Australia, New Zealand, China, South East Asia, Brazil and Mexico.
SEEK Limited
https://www.seek.com.au/
Jane is an information scientist working to improve user access to information on the Web. As Ontology Operations Lead at SEEK, the world’s largest online employment marketplace, Jane and the Ontology Services team manage and make available SEEK’s ontology and related web services for search, enrichment, matching, and analytics.
