Graphs > vectors. Graphs are about relationships and stable sources of truth; vectors merely capture text that has been turned into numbers that are similar to other numbers. When optimizing for ...

Knowledge graph embedding, a highly efficient and scalable approach for link prediction. This paper focuses on distance-based models for KGE, which are lightweight, easy to train, and geometrically ...

This is the official implementation for paper Navigating Labels and Vectors: A Unified Approach to Filtered Approximate Nearest Neighbor Search, which builds the Unified Navigating Graph (UNG) index ...

For use-cases of searching different subsets of vectors in the index, where a non-trivial portion of vectors across fields are overlapping. This could be done today by: Indexing all vectors in a ...

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL Google uses machine learning and graphs to deliver search results. Most search engines do not.

We present a novel upper bound for the optimal index coding rate. Our bound uses a graph theoretic quantity called the local chromatic number. We show how a good local coloring can be used to create a ...