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Communication Dans Un Congrès Année : 2022

KEMA: Knowledge-Graph Embedding Using Modular Arithmetic

Résumé

Knowledge graph is a knowledge representation technique that helps representing entities and relations in a machine understandable way. This promising trend suffers from the problem of incompleteness that was best solved by link prediction. Indeed, link prediction is the most successful method for understanding the structure of the large knowledge graphs. Knowledge graph embedding KGE is one of the best link prediction methods. Its effectiveness is mainly affected by the accuracy of learning representations of entities and relations. In this paper, we propose a new knowledge graph embedding model called KEMA ( Knowledge-graph Embedding using Modular Arithmetic). KEMA has the ability to represent simple and complex relations in an efficient way. Consequently, this allows our model to outperform the majority of the existing models. Mainly, KEMA depends on representing the relations in a knowledge graph by modular arithmetic operations applied between entities. Experimental results on multiple benchmark knowledge graphs verify the accurate
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Dates et versions

hal-03657976 , version 1 (16-06-2022)

Identifiants

  • HAL Id : hal-03657976 , version 1

Citer

Hussein Baalbaki, Hussein Hazimeh, Hassan Harb, Rafael Angarita. KEMA: Knowledge-Graph Embedding Using Modular Arithmetic. The 34th International Conference on Software Engineering and Knowledge Engineering, Jul 2022, Pittsburgh, United States. ⟨hal-03657976⟩

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