KG: Resource List

Recommended Readings:

• J. Z. Pan, An Introduction to Knowledge Graphs, Tutorial at the BIAS2021 summer school, 2021. 
• F. Baader,‎ I. Horrocks,‎ C. Lutz,‎ and U. Sattler: An Introduction to Description Logic. Cambridge University Press 2017.
• J. Z. Pan, G. Vetere, J. M. Gómez-Pérez, H. Wu (Eds.): Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer 2017.
• R. Brachman and H. Levesque: Knowledge Representation and Reasoning. Morgan Kaufmann 2014.
• M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich: A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, Volume 104. 2015.
• D. Ruffinelli, S. Broscheit, and R. Gemulla: You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. ICLR 2020
• W. Hamilton: Graph Representation Learning Book (Chapter 1: Node Embeddings). Morgan & Claypool 2020
• I. Goodfellow, Y. Bengio, and A. Courville: Deep Learning (Chapter 5: Machine Learning Basics; Chapter 7: Regularization for Deep Learning; Chapter 8: Optimization for Training Deep Models; Chapter 15: Representation Learning). MIT Press 2016.

Optional Readings:

• J. Z. Pan, S. Staab, U. Aßmann, J. Ebert,  Y. Zhao (Eds.): Ontology-Driven Software Development. Springer 2013.

Additional Links: 

• Google & KG: Introducing the Knowledge Graph: things, not strings.  What Is the Google Knowledge Graph & How Does It Work?
• Meta & KG: Knowledge Graph Metadata: What Facebook really knows about you?
• Microsoft & KG: Overview of Microsoft Graph Microsoft Academic Knowledge Graph (MAKG)
• Amazon & KG: Knowledge Graphs on AWS Using knowledge graphs to streamline COVID-19 research
• IBM & KG: Use cases of Knowledge Graphs