Knowledge Graph Representation via Similarity-Based Embedding
Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict...
Main Authors: | Zhen Tan, Xiang Zhao, Yang Fang, Bin Ge, Weidong Xiao |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2018-01-01
|
Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2018/6325635 |
Similar Items
-
GTrans: Generic Knowledge Graph Embedding via Multi-State Entities and Dynamic Relation Spaces
by: Zhen Tan, et al.
Published: (2018-01-01) -
Relational Knowledge Prediction via Dynamic Bi-Mode Embedding
by: Yang Fang, et al.
Published: (2018-01-01) -
Patient Similarity via Joint Embeddings of Medical Knowledge Graph and Medical Entity Descriptions
by: Zhihuang Lin, et al.
Published: (2020-01-01) -
Network Embedding via a Bi-Mode and Deep Neural Network Model
by: Yang Fang, et al.
Published: (2018-05-01) -
Representation Learning of Knowledge Graphs with Embedding Subspaces
by: Chunhua Li, et al.
Published: (2020-01-01)