FastKG

FastKG is a knowledge graph embedding training library. Knowledge Graph (KG) embeddings are a way to represent entities and relationships from a KG in a continuous vector space, enabling tasks like link prediction and reasoning. TransE, a popular model, represents relationships as translations in the embedding space, such that for a valid triplet (head, relation, tail), the embedding of the head plus the relation vector is close to the embedding of the tail. Training data for TransE is typically stored in a tab-separated values (TSV) format, where each line represents a triplet, e.g., entity1\trelation1\tentity2. For example a dummy train.tsv should look like this:

Paris   isCapitalOf     France
Einstein    discovered    Relativity

After training, the learned embeddings can be used to infer new triplets by calculating scores for candidate triplets. For instance, given embeddings for Paris, isCapitalOf, and Germany, we can calculate a score to determine if Paris isCapitalOf Germany is likely true.

Acknowledgements

This work has been funded by grants from the National Science Foundation, including the ICICLE AI Institute (OAC 2112606)