Fact Checking via Path Embedding and Aggregation

TL;DR

This paper introduces FEA, a system that uses path embedding and aggregation techniques on knowledge graphs to improve the accuracy of fact-checking by identifying and encoding relevant paths between entities.

Contribution

The paper presents a novel hybrid approach that learns vector representations of paths and aggregates them to enhance fact verification in knowledge graphs.

Findings

Improved fact-checking performance on multiple KGs

Effective path relevance identification and encoding

Hybrid embedding and aggregation strategy benefits

Abstract

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.