Link Prediction using Embedded Knowledge Graphs

Understand

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.