Efficient Localization Using Graph Neural Networks

A computing system, comprising:

• one or more processors;
• and
• memory having program instructions stored therein that are executable by the one or more processors to perform operations that include:
• receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request;
• applying a machine learning language model to the description to determine a description embedding;
• and
• identifying the one or more locations by determining similarities between the description embedding and location embeddings determined using a graph neural network algorithm applied to a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure.

A method, comprising:

• receiving, by a computing system, a data set having a hierarchical structure and descriptions associated with identified locations in the data set;
• assembling, by the computing system and from the data set, a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure;
• applying, by the computing system, a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes;
• and
• training, by the computing system, the graph neural network algorithm based on the generated location embeddings, description embedding representative of the descriptions, and the identified locations.