{"type":"ScholarlyArticle","title":"CPa-WAC: Constellation Partitioning-based Scalable Weighted Aggregation Composition for Knowledge Graph Embedding","authors":[{"name":"Sudipta Modak","slug":"sudipta-modak","given":"Sudipta","family":"Modak","affiliation":"Department of Electrical and Computer Engineering , University of Windsor , ON , Canada; AI Garage , Mastercard , Gurugram , Haryana , India"},{"name":"Aakarsh Malhotra","slug":"aakarsh-malhotra","given":"Aakarsh","family":"Malhotra","affiliation":"AI Garage , Mastercard , Gurugram , Haryana , India"},{"name":"Sarthak Malik","slug":"sarthak-malik","given":"Sarthak","family":"Malik","affiliation":"AI Garage , Mastercard , Gurugram , Haryana , India"},{"name":"Anil Surisetty","slug":"anil-surisetty","given":"Anil","family":"Surisetty","affiliation":"AI Garage , Mastercard , Gurugram , Haryana , India"},{"name":"Esam Abdel-Raheem","slug":"esam-abdel-raheem","given":"Esam","family":"Abdel-Raheem","affiliation":"Department of Electrical and Computer Engineering , University of Windsor , ON , Canada"}],"abstract":"Scalability and training time are crucial for any graph neural network model processing a knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time, the prediction accuracy reduces significantly compared to training the model on the whole graph. In this paper, we propose CPa-WAC: a lightweight architecture that incorporates graph convolutional networks and modularity maximization-based constellation partitioning to harness the power of local graph topology. The proposed CPa-WAC method reduces the training time and memory cost of knowledge graph embedding, making the learning model scalable. The results from our experiments on standard databases, such as Wordnet and Freebase, show that by achieving meaningful partitioning, any knowledge graph can be broken down into subgraphs and processed separately to learn embeddings. Furthermore, these learned embeddings can be used for knowledge graph completion, retaining similar performance compared to training a GCN on the whole KG, while speeding up the training process by upto five times. Additionally, the proposed CPa-WAC method outperforms several other state-of-the-art KG in terms of prediction accuracy.","doi":"10.24963/ijcai.2024/388","url":"https://ijcai.org/proceedings/2024/388","pdf":"https://ijcai.org/proceedings/2024/0388.pdf","markdown":"https://ijcai.org/proceedings/2024/388.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/388","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":388,"pages":{"first":3504,"last":3512},"keywords":["Uncertainty in AI: UAI: Graphical models","Data Mining: DM: Knowledge graphs and knowledge base completion","Machine Learning: ML: Knowledge-aided learning","Knowledge Representation and Reasoning: KRR: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bai, 2023] Yuhe Bai. 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