Knowledge Graph Embeddings
The Lecture has been delivered at the Big Data Analytics Summer School 2020 by Dr. Sahar Vahdati, University of Oxford.
AI and Knowledge Graphs
This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Emanuel Sallinger, University of Oxford.
Reasoning on Financial Knowledge Graphs: The Case of Company Networks
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.
Embedding-based Recommendations on Scholarly Knowledge Graphs
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.
Reasoning in Knowledge Graphs: An Embeddings Spotlight
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.
Swift Logic for Big Data and Knowledge Graphs
Lecture by prof. Georg Gottlob, University of Oxford at the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
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.