In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.

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.

Nowadays, representing entities and relations in a machine understandable way through Knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation patterns such as symmetry, anti-symmetry, inversion, and composition. However, there are few models in the literature that take into consideration the modeling of complex relation patterns like 1-[Formula: see text], [Formula: see text]-1 and [Formula: see text]-[Formula: see text], which are common in real-world applications. To overcome this challenge, this paper presents a new KGE model called KEMA[Formula: see text], i.e. KGE using Modular Arithmetic, that relies on the combination of projection and modular arithmetic. The main idea behind KEMA[Formula: see text] is to project the entities of a relation to represent the relations of a KG, before applying a modular arithmetic over it. Thus, KEMA[Formula: see text] will be able to infer all simple and complex relation patterns for any KGE applications. Through extensive experiments on several datasets, we demonstrated the relevance of KEMA[Formula: see text] in terms of effectively representing all model relations in a KG. Simulations on several tested datasets show that KEMA[Formula: see text] obtains the good scores for Mean Rank (MR) and Hits@1 tests. Moreover, KEMA[Formula: see text] obtains the good Hits@1 score compared to the existing models.