Discovery

This module includes a number of functions to perform knowledge discovery in graph embeddings.

Functions provided include discover_facts which will generate candidate statements using one of several defined strategies and return triples that perform well when evaluated against corruptions, find_clusters which will perform link-based cluster analysis on a knowledge graph, find_duplicates which will find duplicate entities in a graph based on their embeddings, and query_topn which when given two elements of a triple will return the top_n results of all possible completions ordered by predicted score.

discover_facts(X, model[, top_n, strategy, …])	Discover new facts from an existing knowledge graph.
find_clusters(X, model[, …])	Perform link-based cluster analysis on a knowledge graph.
find_duplicates(X, model[, mode, metric, …])	Find duplicate entities, relations or triples in a graph based on their embeddings.
query_topn(model[, top_n, head, relation, …])	Queries the model with two elements of a triple and returns the top_n results of all possible completions ordered by score predicted by the model.
find_nearest_neighbours(kge_model, entities)	Return the nearest neighbors of entities.