Fact Checking via Path Embedding and Aggregation

2020-11-16 · Giuseppe Pirrò

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.

📄 PDF Abstract BibTeX arXiv:2011.08028

Code (0)

등록된 구현이 없습니다.

Tasks

Fact Checking Knowledge Graphs

HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs

2024-09-10 · Umair Qudus, Michael Roeder, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo

We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, …

Diversity Ensemble Learning Fact Checking Feature Engineering+1

Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking

2023-10-11 · Gustav Nikopensius, Mohit Mayank, Orchid Chetia Phukan, Rajesh Sharma

Fact-checking is a crucial task as it ensures the prevention of misinformation. However, manual fact-checking cannot keep up with the rate at which false information is generated and disseminated online. Automated fact-c…

Fact Checking Misinformation reinforcement-learning Reinforcement Learning+1

Message-passing selection: Towards interpretable GNNs for graph classification

2023-06-03 · Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang 외

In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing expla…

Classification Graph Classification

Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies

2024-12-06 · Recep Firat Cekinel, Pinar Karagoz, Cagri Coltekin

This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. To be more specific, we investigate whether incorporating multimodal content imp…

Fact Checking Misinformation Veracity Classification

Checking Fact Worthiness using Sentence Embeddings

2020-12-16 · Sidharth Singla

Checking and confirming factual information in texts and speeches is vital to determine the veracity and correctness of the factual statements. This work was previously done by journalists and other manual means but it i…

Fact Checking Information Retrieval Retrieval Sentence+1