  Since large knowledge bases are typically incomplete, missing facts need to
be inferred from observed facts in a task called knowledge base completion. The
most successful approaches to this task have typically explored explicit paths
through sequences of triples. These approaches have usually resorted to
human-designed sampling procedures, since large knowledge graphs produce
prohibitively large numbers of possible paths, most of which are uninformative.
As an alternative approach, we propose performing a single, short sequence of
interactive lookup operations on an embedded knowledge graph which has been
trained through end-to-end backpropagation to be an optimized and compressed
version of the initial knowledge base. Our proposed model, called Embedded
Knowledge Graph Network (EKGN), achieves new state-of-the-art results on
popular knowledge base completion benchmarks.
