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

This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Emanuel Sallinger, University of Oxford.

The initial release of KGs was started on an industry scale by Google and further continued with the publication of other large-scale KGs such as Facebook, Microsoft, Amazon, DBpedia, Wikidata and many more. As an influence of the increasing hype in KG and advanced AI-based services, every individual company or organization is adapting to KG. The KG technology has immediately reached industry, and big companies have started to build their own graphs such as the industrial Knowledge Graph at Siemens.

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.

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.

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.
 

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