Fact Checking via Path Embedding and Aggregation
  Knowledge graphs (KGs) are a useful source of background knowledge to
(dis)prove facts of the form (s, p, o). Finding paths between s and o is the
cornerstone of several fact-checking approaches. While paths are useful to
(visually) explain why a given fact is true or false, it is not completely
clear how to identify paths that are most relevant to a fact, encode them and
weigh their importance. The goal of this paper is to present the Fact Checking
via path Embedding and Aggregation (FEA) system. FEA starts by carefully
collecting the paths between s and o that are most semantically related to the
domain of p. However, instead of directly working with this subset of all
paths, it learns vectorized path representations, aggregates them according to
different strategies, and use them to finally (dis)prove a fact. We conducted a
large set of experiments on a variety of KGs and found that our hybrid solution
brings some benefits in terms of performance.
