KEMA: Knowledge-Graph Embedding Using Modular Arithmetic
Hussein Baalbaki, Hussein Hazimeh, Hassan Harb, Rafael Angarita · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2022
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 representation, low complexity and scalability of KEMA.