CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding
This work addresses entity type prediction for knowledge graph research, but it appears incremental as it builds on existing embedding models.
The authors tackled the problem of entity type prediction in knowledge graphs by proposing CORE, a method that combines complex space embeddings (RotatE and ComplEx) with a regression model, and it outperformed benchmarking methods on representative datasets.
Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.