I’ve recently been working with the Weaviate team on Vector Search Engines. One of our projects that I’m the most excited about is creating Web Demos where anyone across the world can see the power of Neural Search for themselves -- for free.
I’m really to excited to share that there are 2 new web demos in Weaviate
Wikidata Weaviate Demo (subject of this video): [ Ссылка ]
Demo: [ Ссылка ]
Wikipedia Weaviate Demo: [ Ссылка ]
This video will explains all sorts of things around this such as: (1) the difference between Wikidata and Wikipedia, (2) how graph embeddings are computed with Facebook AI’s PyTorch-BigGraph, (3) a visualization of the demo itself, and (4) a conclusion where I present my ideas on how I’m using graph-structured data and how I’m currently about thinking about the field of Graph Neural Networks and Knowledge Graphs.
Thank you so much for watching, I really hope you enjoy this presentation and would be more than happy to answer any questions you might have about these ideas.
Content Links:
Wikidata Weaviate Demo:
Wikipedia Weaviate Demo:
Wikidata Properties: [ Ссылка ]
PyTorch-BigGraph: [ Ссылка ]
Deep Learning for Graph-Structured Representations, Thomas Kipf PhD Thesis: [ Ссылка ]
Chapters
0:00 Intro
0:48 Wikidata versus Wikipedia
4:10 Word2Vec versus BERT
5:05 PyTorch-BigGraph
7:07 Wikidata Knowledge Graph
8:00 Weaviate Demo
10:07 Nearest Neighbors to “Deep Learning”
10:52 Graph Vector Search
11:08 Drug Repurposing Graphs
12:22 Citation Network Keras Example
13:53 KerasBERT and Graph Data
15:48 Thoughts on Graph Embeddings
19:55 Weaviate Web Demos!
