Entity Typing Based on RDF2Vec Using Supervised and Unsupervised Methods

Radina Sofronova, Russa Biswas, Mehwish Alam, Harald Sack

Abstract: Knowledge Graphs have been recognized as the foundation for diverse applications in the field of data mining, information retrieval, and natural language processing. So the completeness and the correctness of the KGs are of high importance. The type information of the entities in a KG, is one of the most vital facts. However, it has been observed that type information is often noisy or incomplete. In this work, the task of fine-grained entity typing is addressed by exploiting the pre-trained RDF2Vec vectors using supervised and unsupervised approaches.