Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products

TL;DR

This paper introduces HaCKG, a knowledge graph-based framework that models complex relations between cosmetics and ingredients to improve halal status prediction, surpassing existing image-based methods.

Contribution

The paper presents a novel knowledge graph and a pre-trained relational graph attention network for predicting halal status of cosmetics, capturing high-order relations ignored by prior approaches.

Findings

HaCKG outperforms state-of-the-art baselines in halal prediction accuracy.

The knowledge graph effectively models complex relations between cosmetics and ingredients.

Pre-trained relational graph attention network enhances relation learning for better predictions.

Abstract

The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on analyzing the discrete and specific ingredients within separate cosmetics, which ignore the high-order and complex relations between cosmetics and ingredients. To address this problem, we propose a halal cosmetic recommendation framework, namely HaCKG, that leverages a knowledge graph of cosmetics and their ingredients to explicitly model and capture the relationships between cosmetics and their components. By representing cosmetics and ingredients as entities within the knowledge graph, HaCKG effectively learns the high-order and complex relations between entities, offering a robust method for predicting halal status. Specifically, we first construct a cosmetic knowledge graph representing the relations between various cosmetics, ingredients, and their properties. We then propose a pre-trained relational graph attention network model with residual connections to learn the structural relation between entities in the knowledge graph. The pre-trained model is then fine-tuned on downstream cosmetic data to predict halal status. Extensive experiments on the cosmetic dataset over halal prediction tasks demonstrate the superiority of our model over state-of-the-art baselines.