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

2025-01-10

AI Summary

Existing halal cosmetic classification methods rely solely on individual ingredient features, neglecting semantic relationships among ingredients, thereby limiting prediction accuracy. Method: This paper proposes a knowledge graph–based halal compliance prediction framework. It introduces knowledge graph completion to this domain for the first time, constructing a cosmetics–ingredients knowledge graph to explicitly model high-order semantic relations. We design a pretraining–fine-tuning residual graph attention network (RGAT) that jointly encodes multi-source ingredient interactions and cultural constraints. Contribution/Results: Evaluated on a dedicated halal cosmetic dataset, our method achieves a 6.2% absolute accuracy improvement over the state of the art. Results demonstrate that structured relational modeling significantly enhances reasoning for culturally sensitive products. This work establishes a novel paradigm for cross-cultural AI applications, bridging domain-specific knowledge representation with deep learning for regulatory compliance assessment.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Halal Certification
• Cosmetic Ingredients
• Machine Learning

Innovation

• HaCKG Recommendation System
• Cosmetic Ingredient Graphs
• Halal Prediction