Identifying relevant common sense information in knowledge graphs

Code

• github.com/guyaglionby/kg-common-sense-extraction
  OfficialIn paperpytorch★ 0

Abstract

Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.

Tasks

Reproductions