Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable.
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.
In this lecture, we introduce the aspect of reasoning in Knowledge Graphs. We give a broad overview focusing on the multitude of reasoning techniques: spanning logic-based reasoning, embedding-based reasoning, neural network-based reasoning, etc. In particular, we discuss three dimensions of reasoning in Knowledge Graphs. Complementing these dimensions, we will structure our exploration based on a pragmatic view of reasoning tasks and families of reasoning tasks: reasoning for knowledge integration, knowledge discovery and application services.
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.
This deliverable presents a sustainability analysis for the project LAMBDA (Learning, Applying, Multiplying Big Data Analytics), funded by "Spreading Excellence and Widening Participation" Work Programme 2016-2017. The project pursued two main goals:
Funded by "Spreading Excellence and Widening Participation" Work Programme 2016-2017, the
LAMBDA (Learning, Applying, Multiplying Big Data Analytics) project pursuit two main goals:
One of the specific objectives of LAMBDA (Learning, Applying, Multiplying Big Data Analytics) project is focused its efforts on formulating and delivering proper strategies and activities for dissemination and communication that will result in the best and most effective advertising of the project at local, regional and European, and international level.
One of the specific objectives of LAMBDA (Learning, Applying, Multiplying Big Data Analytics) project is focused its efforts on formulating and delivering proper strategies and activities for dissemination and communication that will result in the best and most effective advertising of the project at local, regional and European, and international level.
One of the goals of the LAMBDA project is outreach and engagement with different types of stakeholders including the wider public. In the very beginning, the LAMBDA Consortium defined a Stakeholder Engagement Strategy as a set of actions for the involvement of external stakeholders and alignment of joint activities with LAMBDA objectives and priorities defined in the Grant Agreement.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 809965.
