LAMBDA training materials will include formal conceptual frameworks for designing and maintaining knowledge graphs; such as strategies for the semiautomatic construction of such graphs from the combination of proprietary enterprise data and relevant public domain knowledge; opportunities and implications in terms of performance and access control.

LAMBDA training materials will include approaches for better supporting the variety dimension of Big Data comprising RDF, RDF-Schema and OWL knowledge representation formalisms, mapping standards such as R2RML, JSON-LD and CSVW, the SPARQL query language etc. Integrating semantic and Big Data technologies can help making Big Data architectures and applications more flexible, adaptive and their implementation more efficient.

LAMBDA training materials will include different algorithms and tools related to Distributed Semantic Analytics, Semantic Question Answering, Structured Machine Learning, Deep Learning, Software Engineering for Data Science, Semantic Data Management, Knowledge Extraction and Validation.