Mohammad Javad Saeedizade


Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction
Mehdi Mirzapour | Waleed Ragheb | Mohammad Javad Saeedizade | Kevin Cousot | Helene Jacquenet | Lawrence Carbon | Mathieu Lafourcade
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.

pdf bib
KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods
Mohammad Javad Saeedizade | Najmeh Torabian | Behrouz Minaei-Bidgoli
Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

Link Prediction is the task of predicting missing relations between knowledge graph entities (KG). Recent work in link prediction mainly attempted to adapt a model to increase link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources. This paper proposes the refinement of knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models have significantly less complexity than deep learning approaches; this motivated us to improve their accuracy. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, and Mean Reciprocal Rank.