# Knowledge Graphs

**Authors:** Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann  
**arXiv:** [2003.02320](https://arxiv.org/abs/2003.02320)  
**Date:** 2020-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2003.02320v1/

## Key points

- Knowledge graphs have gained significant attention from industry and academia for their ability to exploit diverse and dynamic large-scale collections of data.
- The paper provides a comprehensive introduction to knowledge graphs, discussing various graph-based data models and query languages used in this context.
- Schema and identity play essential roles in representing and extracting knowledge through deductive and inductive techniques within knowledge graphs.
- Methods for creating, enriching, assessing quality, refining, and publishing knowledge graphs are outlined in the paper.
- Prominent open-source and enterprise knowledge graph platforms are discussed along with their applications and how they leverage different techniques.
- The authors highlight high-level future research directions for knowledge graphs in various domains such as artificial intelligence (AI), databases (DB), and machine learning (ML).

## Summary

In their paper "Knowledge Graphs," authors Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres,
Roberto Navigli, Axel-Cyrille Ngonga Ngomo,
Sabbir M. Rashid,
Anisa Rula,
Lukas Schmelzeisen,
Juan Sequeda,
Steffen Staab,
and Antoine Zimmermann provide a comprehensive introduction to knowledge graphs. <br>
 have recently gained significant attention from both industry and academia due to their ability to exploit diverse and dynamic large-scale collections of data. The paper begins with a general introduction to  and goes on to discuss various graph-based data models and query languages used in this context. The authors delve into the roles of schema and identity within , explaining how they are essential for representing and extracting knowledge through a combination of deductive and inductive techniques. Furthermore,the paper outlines methods for creating,enriching,and assessing quality refining and publishing . It provides an overview of prominent open , as well as enterprise , along with their applications and how they leverage the aforementioned techniques. The authors conclude by highlighting high-level future research directions for <kd>knowledge graphs.</ kd>This extensive 130-page paper serves as a valuable resource for those interested in understanding the intricacies of < kd >knowledge graphs</ kd >and their applications in various domains such as artificial intelligence (AI), databases (DB), and machine learning (LG).