Introducing RezoJDM16k: a French Knowledge Graph DataSet for Link Prediction

International Conference on Language Resources and Evaluation (LREC)(2022)

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Abstract
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 (Lafourcade, 2007). 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. In addition, we provide strong knowledge graph embedding baselines that are used in link prediction task 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.
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Key words
language resource, knowledge graph dataset, link prediction, knowledge graph embedding, knowledge graph completion, lexical-semantic network
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