Big Data Analytics for Semantic Data
Half-Day Tutorial at the 20th European Semantic Web Conference

Tutorial information

Researchers, scientists and companies alike increasingly leverage semantically enriched, linked datasets to train machine learning models for tasks ranging from discovering new vaccines and materials, to recommending products and services, to building virtual personal assistants. At the same time, big-data analytics engines are increasingly adopted to store and process the ever increasing volumes of data efficiently at scale. Until recently however, the Semantic Web, big data analytics and machine learning communities were separated, since big-data analytics engines could not process Knowledge Graphs (KGs).

This tutorial aims to provide an up to date overview of recent advances that allow end to end processing pipelines to be constructed so that analytics and machine learning tasks can be performed without the need for intermediate, computationally expensive and/or time consuming data transfers and/or transformations. Hands on activities covering statistical analytics and inferencing over KGs, using simple use cases will be provided.

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