Collaborative Policy Learning for Open Knowledge Graph Reasoning

Understand

In recent years, there has been a surge of interests in interpretable graph reasoning methods.

• However, these models often suffer from limited performance when working on sparse and incomplete graphs, due to the lack of evidential paths that can reach target entities.
• Here we study open knowledge graph reasoning---a task that aims to reason for missing facts over a graph augmented by a background text corpus.
• A key challenge of the task is to filter out "irrelevant" facts extracted from corpus, in order to maintain an effective search space during path inference.