Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products
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 method...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838562/ |
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| author | 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 |
| author_facet | 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 |
| author_sort | Van Thuy Hoang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fab68e7367e94e36bc16982b92cebc6e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fab68e7367e94e36bc16982b92cebc6e2025-01-28T00:01:37ZengIEEEIEEE Access2169-35362025-01-0113151581516710.1109/ACCESS.2025.352848810838562Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily ProductsVan Thuy Hoang0https://orcid.org/0000-0003-4094-1123Tien-Bach-Thanh do1https://orcid.org/0000-0002-3068-3338Jinho Seo2https://orcid.org/0009-0002-7729-3801Seung Charlie Kim3Luong Vuong Nguyen4https://orcid.org/0000-0002-4680-1984Duong Nguyen Minh Huy5https://orcid.org/0009-0000-7439-5736Hyeon-Ju Jeon6https://orcid.org/0000-0002-2400-8360O-Joun Lee7https://orcid.org/0000-0001-8921-5443Department of Artificial Intelligence, The Catholic University of Korea, Bucheon-si, Gyeonggi-do, Republic of KoreaDepartment of Artificial Intelligence, The Catholic University of Korea, Bucheon-si, Gyeonggi-do, Republic of KoreaSchool of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon-si, Gyeonggi-do, Republic of KoreaShukran Korea Company Ltd., Seoul, Republic of KoreaFPT University, Danang Campus, Da Nang, VietnamFPT University, Danang Campus, Da Nang, VietnamData Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, Republic of KoreaDepartment of Artificial Intelligence, The Catholic University of Korea, Bucheon-si, Gyeonggi-do, Republic of KoreaThe 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.https://ieeexplore.ieee.org/document/10838562/Cosmetic knowledge graph embeddinghalal cosmetic predictionrelational graph attention networks |
| spellingShingle | 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 Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products IEEE Access Cosmetic knowledge graph embedding halal cosmetic prediction relational graph attention networks |
| title | Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products |
| title_full | Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products |
| title_fullStr | Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products |
| title_full_unstemmed | Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products |
| title_short | Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products |
| title_sort | halal or not knowledge graph completion for predicting cultural appropriateness of daily products |
| topic | Cosmetic knowledge graph embedding halal cosmetic prediction relational graph attention networks |
| url | https://ieeexplore.ieee.org/document/10838562/ |
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