论文标题
通过路径嵌入和聚集进行事实检查
Fact Checking via Path Embedding and Aggregation
论文作者
论文摘要
知识图（kgs）是（dis）证明形式（s，p，o）的事实的有用背景知识来源。寻找S和O之间的路径是几种事实检查方法的基石。虽然（视觉上）路径很有用，解释了为什么给定的事实是真实或错误的，但尚不完全清楚如何识别与事实最相关的路径，对它们进行编码并权衡其重要性。本文的目的是通过路径嵌入和聚合（FEA）系统介绍事实检查。 FEA首先要仔细收集与p的域最相关的S和O之间的路径。但是，它没有直接使用所有路径的子集，而是学习矢量路径表示，而是根据不同的策略进行汇总，并使用它们最终（Dis）证明了事实。我们对各种公斤进行了大量实验，发现我们的混合解决方案在性能方面带来了一些好处。
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.
