FactKG: Fact Verification via Reasoning on Knowledge Graphs

TL;DR

FactKG introduces a large dataset for fact verification that leverages reasoning over knowledge graphs, encompassing diverse claim types and linguistic styles, to enhance the reliability and practicality of KG-based verification methods.

Contribution

The paper presents a new dataset, FactKG, with 108k claims and multiple reasoning types, and provides baseline methods for KG-based fact verification.

Findings

FactKG covers five reasoning types including multi-hop and negation.

Baseline models demonstrate the dataset's challenge and potential for improvement.

The dataset includes colloquial and written style claims for practical applications.

Abstract

In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not been adequately utilized as a knowledge source. KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability. A KG consists of nodes and edges which makes it clear how concepts are linked together, allowing machines to reason over chains of topics. However, there are many challenges in understanding how these machine-readable concepts map to information in text. To enable the community to better use KGs, we introduce a new dataset, FactKG: Fact Verification via Reasoning on Knowledge Graphs. It consists of 108k natural language claims with five types of reasoning: One-hop, Conjunction, Existence, Multi-hop, and Negation. Furthermore, FactKG contains various linguistic patterns, including colloquial style claims as well as written style claims to increase practicality. Lastly, we develop a baseline approach and analyze FactKG over these reasoning types. We believe FactKG can advance both reliability and practicality in KG-based fact verification.