.png){kind=icon}
.png){kind=icon}
Return
CORE: A knowledge graph entity type prediction method via complex space regression and emb e dding
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2022.03.024.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE ( CO mplex space R egression and E mbedding), 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. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Knowledge graph
Complex space embedding
Entity type prediction
Self-adversarial negative samplin
Multi-label classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W