Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products
Van Thuy Hoang, Tien-Bach-Thanh Do, Jinho Seo, Seung Charlie Kim,, Luong Vuong Nguyen, Duong Nguyen Minh Huy, Hyeon-Ju Jeon, O-Joun Lee

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…
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Taxonomy
TopicsHalal products and consumer behavior · Culinary Culture and Tourism
MethodsSoftmax · Attention Is All You Need · Focus
