Course talk:CPSC522/Knowledge Graphs

The article is about KGs, and is interesting.

Firstly, the article mentions using low-dimensional embeddings as a common approach to link prediction but does not provide any information on how these embeddings are generated or how they are used to make predictions. \\

Secondly, the KG Completions para mentions using deep reinforcement learning for link prediction by finding relational paths, but does not provide any explanation on how this approach works. It could be helpful to provide more technical details on how deep reinforcement learning can be used to find these paths and how they are used to make predictions.

Rest is all good and very well written.

The article on knowledge graphs is impressive, providing a clear explanation of the concept and its representation. I appreciate the range of applications highlighted in the article.

I think the article should acknowledge some of the challenges and limitations of knowledge graphs. For instance, limited data sources and capturing the full complexity of information are some of the drawbacks of knowledge graphs. Additionally, ethical considerations such as privacy concerns and potential biases in data could have been discussed.

(Just a suggestion) To make the article more informative and engaging, the author could consider discussing current trends and applications of knowledge graphs in fields like natural language processing and artificial intelligence. Overall, the article is well-structured and easy to comprehend, making it an excellent introduction to knowledge graphs.