Abstract
This paper proposes a method to find potentially valid menus which do not explicitly exist in the recipe dataset using knowledge graph embedding (KGE).KGE can predict the missing links in Knowledge Graphs (KGs) by embedding each entity and relation as a vector. Using this feature,the proposed method finds potentially valid menus from the recipe dataset. As it is impossible for a recipe KG to include all possible combinations of recipes that could be regarded as menus,finding potentially valid menus is necessary for realizing recipe recommender systems.This paper describes how to construct the recipe KG from the Cookpad dataset and find potentially valid menus by exploiting TransE.The effectiveness of the proposed method is shown based on the survey-based evaluation. Furthermore, this paper investigates the effect of additional learning.
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References
Cookpad data. Informatics Research Data Repository, National Inst. Inform. (dataset). https://doi.org/10.32130/idr.5.1
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling Multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS26). vol. 2, pp. 2787–2795 (2013)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the 25th Annual Conference on Artificial Intelligence(AAAI). vol. 32, pp. 1811–1818 (2018). https://doi.org/10.1609/aaai.v32i1.11573
Ebisu, T., Ichise, R.: TorusE: Knowledge graph embedding on a lie group. In: Proceedings of the 25th Annual Conference on Artificial Intelligence(AAAI). vol. 32, pp. 1819–1826 (2018). https://doi.org/10.1609/aaai.v32i1.11538
Gharibi, M., Zachariah, A., Rao, P.: FoodKG: a tool to enrich knowledge graphs using machine learning techniques. Frontiers in Big Data 3, 12 (2020). https://doi.org/10.3389/fdata.2020.00012
Gong, F., Wang, M., Wang, H., Wang, S., Liu, M.: SMR: Medical knowledge graph embedding for safe medicine recommendation. Big Data Res. 23, 100174 (2021). https://doi.org/10.1016/j.bdr.2020.100174
Harashima, J., Hiramatsu, M.: Cookpad parsed corpus: Linguistic annotations of japanese recipes. In: Proceedings of the 14th Linguistic Annotation Workshop(Law), pp. 87–92 (2020). https://api.semanticscholar.org/CorpusID:227230692
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 25th Annual Conference on Artificial Intelligence(AAAI). vol. 29, pp. 2181–2187 (2015). https://doi.org/10.1609/aaai.v29i1.9491
Nickel, M., Tresp, V., Kriegel, H.P.: A Three-Way model for collective learning on Multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning(ICML), pp. 809–816 (2011)
Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Translational models for item recommendation. In: Proceedings of the 17th International Semantic Web Conference(ISWC), pp. 478–490 (2018)
Ruffinelli, D., Broscheit, S., Gemulla, R.: You CAN teach an old dog new tricks! on training knowledge graph embeddings. In: Proceedings of the 8th International Conference on Learning Representations(ICLR) (2020). https://openreview.net/forum?id=BkxSmlBFvr, 20 pages
Sakib, M.S., Paulius, D., Sun, Y.: Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking. IEEE Robotics Autom. Lett.(RA-L) 7(4), 11492–11499 (2022). https://doi.org/10.1109/LRA.2022.3191068
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Proceedings of the 17th International Semantic Web Conference(ISWC), pp. 593–607 (2018)
Tian, Y., Zhang, C., Guo, Z., Ma, Y., Metoyer, R., Chawla, N.: Recipe2vec: Multi-modal recipe representation learning with graph neural networks. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence(IJCAI), pp. 3448–3454 (2022). https://doi.org/10.24963/ijcai.2022/479
Tian, Y., Zhang, C., Metoyer, R., Chawla, N.V.: Recipe recommendation with hierarchical graph attention network. Frontiers in Big Data 4, 778417 (2022). https://doi.org/10.3389/fdata.2021.778417
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 21st Annual Conference on Artificial Intelligence(AAAI). vol. 28, pp. 1112–1119 (2014). https://doi.org/10.1609/aaai.v28i1.8870
Acknowledgements
In this paper, we used “Cookpad dataset” provided by Cookpad Inc. via IDR Dataset Service of National Institute of Informatics. This work was partly supported by JSPS KAKENHI Grant Numbers 21H03553, 22H03698, and 22K19836.
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Ohta, A., Shibata, H., Takama, Y. (2024). Proposal of Finding Potentially Valid Menus from Recipe Dataset Using Knowledge Graph Embedding. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_3
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