Entity type is a very important piece of information in Knowledge Graphs. Researchers have leveraged entity type information to get better results in many Knowledge Graph related tasks such as link prediction. Besides, entity type is also important for Information Extraction tasks including entity linking and relation extraction. However, Knowledge Graph entity type information is often incomplete and noisy. Therefore, there is a need to develop effective algorithms for predicting missing types for entities.

Knowledge Graph (KG) Embeddings in complex vector space have demonstrated superior performance in relation prediction and triple classification. Representing entities and relations in complex space has several advantages than traditional models such as better expressive power, and better capabilities of modeling one-to-many and asymmetric relations. We leverage these characteristics of complex KG Embeddings and formulate the type prediction problem as a complex space regression problem. Experimental results confirm our hypothesis that the expressiveness of embedding models correlates with the performance on type prediction. Our newly proposed method achieves state-of-the-art results in type prediction for many benchmarking datasets.

[1] Sun, Zhiqing, et al. “Rotate: Knowledge graph embedding by relational rotation in complex space.” arXiv preprint arXiv:1902.10197 (2019).

[2] Zhao, Yu, et al. “Connecting embeddings for knowledge graph entity typing.” arXiv preprint arXiv:2007.10873 (2020).

— by Xiou Ge