Ensuring the semantic interoperability issues between heterogeneous systems is very challenging task.  Using a common semantic data model, is a central element to ensure this semantic interoperability. In fact, a common semantic data model allows heterogenous systems to share the same meaning of entities that enables them to interoperate. Ontologies are recognized as the corner stone element to build a common semantic model.

The Lecture has been delivered at the Big Data Analytics Summer School 2020 by Dr. Sahar Vahdati, University of Oxford.

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.
 

Mikhail Galkin (Fraunhofer IAIS) gave a Lecture as part of the CS520 Knowledge Graphs Course at Stanford University, see PPT or video.

This Lecture introduces how Knowledge Graphs are generated. The goal is to gain

Lecture  by prof. Georg Gottlob, University of Oxford at the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)

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.

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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.

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