Knowledge Extraction

Tuesday, September 10, 2019 - 12:15 to 13:15
Kārlis Čerāns


Towards A Scalable Semantic-based Distributed Approach for SPARQL query evaluation

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

Normalising the long tail of role titles in an online employment marketplace

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.