Vector Search through Wikidata with Weaviate

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): https://github.com/semi-technologies/biggraph-wikidata-search-with-weaviate Demo: http://biggraph-wikidata-in-weaviate.vectors.network/ Wikipedia Weaviate Demo: https://github.com/semi-technologies/semantic-search-through-Wikipedia-with-Weaviate 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: https://www.wikidata.org/wiki/Help:Properties PyTorch-BigGraph: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/ Deep Learning for Graph-Structured Representations, Thomas Kipf PhD Thesis: https://pure.uva.nl/ws/files/46900201/Thesis.pdf 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!