embeddingdb 0.0.1

Uploading Entity Embeddings

Entities can be embedded and stored from various types of representation learning, including network representation learning, knowledge graph embedding, and textual learning.

Upload embeddings generated by word2vec by specifying the file path with:

$ embeddingdb upload --fmt word2vec --path ~/path/to/file.txt

Upload embeddings generated by pykeen by specifying the output directory with:

$ embeddingdb upload --fmt keen --path ~/path/to/directory/

Listing Entity Embeddings

After uploading, the collections can be listed with:

$ embeddingdb ls

Analyzing Entity Embeddings’ Correlations

One of the motivations for building this repository was to make a convenient way to compare the embeddings for entities generated through orthogonal embedding tecnhiques. For example, we wanted to know to what extent the embeddings for proteins generated from their sequences with ratvec contained the same information as the embeddings generated from protein-protein interaction networks with pykeen or nrl.

The two positional arguments correspond to the collection identifiers in the database.

$ embeddingdb analyze 1 2