This module will cover the needs and challenges of distributed analytics and then dive into the details of scalable semantic analytics stack (SANSA) used to scalable analytics for knowledge graphs. This module will cover the setup, Apis and different layer of SANSA. At the end of this module, the audience will be able to execute examples and create programs that use SANSA APIs.

Linked open data sources and the semantic web has become a precious data source for data analytics tasks and data integration. The growing data set sizes of RDF Knowledge Graph data need scalable processing and analytics techniques. The processing power of in-memory frameworks which can perform scalable distributed semantic analytics like SANSA, make use of Apache Spark and Apache Jena to provide start-to-end extensive scalable analytics on RDF knowledge graphs.

The last decades witnessed a significant evolution in terms of data generation, management, and maintenance, especially in the RDF format. Moreover, in the energy domain, semantic data is finding its way and can be used for various data

The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and the evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allows to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains.

The size and number of knowledge graphs have increased tremendously in recent years. In the meantime, the distributed data processing technologies have also advanced to deal with big data and large scale knowledge graphs. This lecture introduces Scalable Semantic Analytics Stack (SANSA), which addresses the challenge of dealing with large scale RDF data and provides a uni ed framework for applications like link prediction, knowledge base completion, querying, and reasoning.

Big data is a reality and it is being generated and handled in almost all digitised scenarios. This chapter covers the history of Big data and discusses prominent related terminologies. The significant technologies including architectures and tools are reviewed. Finally, the lecture reviews big knowledge graphs, that attempt to address the challenges (e.g. heterogeneity, interoperability, variety) of big data through their specialised representation format. This chapter aims to provide an overview of the existing terms and technologies related to big data.