Big Data Analytics Summer School, June 2019
We're pleased to announce that the Big Data Analytics Summer School was organized in Belgrade, Serbia at the Mihajlo Pupin Institute, from 18 to 29 June 2019. For more information, please check Past Events
LAMBDA Plenary #2, June 2019
We're pleased to announce that the LAMBDA Plenary meeting was organized in Belgrade, Serbia at the Mihajlo Pupin Institute, on 17 June 2019. For more information, please check Past Events
Data Management Plan
This document entitled “Data Management Plan” (DMP) outlines the strategy for data management to be applied throughout the course of the LAMBDA project, as well as the actions that will be taken after the LAMBDA project has been finished. It is based on the “Guidelines on Data Management in Horizon 2020” document and follows the FAIR Data management principles. The DMP will be updated in the course of the project whenever significant changes arise, in addition to the periodic evaluation/assessment of the project.
Quality control, Risk management and Self-assessment (Plan)
This deliverable sets out the quality practices for the LAMBDA project and provides assurance that the quality control and risk management requirements are planned appropriately. It is the first deliverable of Task 1.3 (quality control, risk management and self-assessment) which monitors the project implementation in order to work effectively towards achieving the project goals. Quality control, risk management and self-assessment procedures defined in the project proposal phase have been further elaborated and presented in this deliverable.
Extraction for Knowledge Graphs
This module will discuss the topic of extraction for Knowledge Graphs. We will focus on web data extraction in this module. Web data extraction is essential to make information available on the web accessible and usable by Knowledge Graphs. We provide a thorough introduction to the topic. This will feature both Oxford’s Vadalog and OXPath systems.
Download paper.
Reasoning in Knowledge Graphs
This lecture discusses reasoning in Knowledge Graphs. Reasoning is essential to gain value from Knowledge Graphs by deriving insights and making available new implicit data from existing data. We will cover the theory and practice of reasoning in Knowledge Graphs, and provide a number of easily accessible examples based on Oxford’s Vadalog system.
Introduction to Knowledge Graphs
Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies. Many defnitions exist of what a Knowledge Graph is, and in this chapter, we are going to take the position that precisely in the multitude of definitions lies one of the strengths of the area. We will choose a particular perspective, which we will call the layered perspective and three views on Knowledge Graphs:
- KGs as Knowledge Representation Tools
- KGs as Knowledge Management Systems
- KGs as Knowledge Application Services
Distributed Semantic Analytics II
This module will cover the setup, APIs and different layers of SANSA. At the end of this module, the audience will be able to execute examples and create programs that use SANSA APIs. The final part of this lecture is planned to be an interactive session to wrap up the introduced concepts and present attendees some open research questions which are nowadays studied by the community.
Distributed Semantic Analytics I
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 perform scalable analytics for knowledge graphs. It will cover different SANSA layers and the underlying principles to achieve scalability for knowledge graph processing.
Please, download from the following link.
