Results 31 to 40 of about 8,049,750 (250)

MöbiusE: Knowledge Graph Embedding on Möbius ring

, 2021
In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring.
Xiong, W, Liu, J, Wen, S, Chen, Y, Zhang, Z, Wenjun Xiong, Yao Chen, Shiping Wen, Zhe Zhang, Jiangang Liu   +9 more
core   +1 more source

Spectral Geometry for Structural Pattern Recognition [PDF]

, 2010
Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis
El Ghawalby, Hewayda
core   +5 more sources

A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution

Applied Sciences, 2019
Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge ...
Yanjun Wang, Yaqiong Qiao, Jiangtao Ma, Guangwu Hu, Chaoqin Zhang, Arun Kumar Sangaiah, Hongpo Zhang, Kai Ren   +7 more
doaj   +1 more source

SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph

IEEE Access, 2021
Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations.
Jingbin Wang, Kuan Nie, Xinyuan Chen, Jing Lei   +3 more
doaj   +1 more source

From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding [PDF]

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph ...
Zhang, He, Gao, Longxiang, Jin, Jiong, Xiang, Yong, Wu, Di, Cai, Borui, Luan, Tom   +6 more
core   +1 more source

CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding

Mathematics, 2022
Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph.
Pengfei Zhang, Dong Chen, Yang Fang, Xiang Zhao, Weidong Xiao   +4 more
doaj   +1 more source

Subsampling for Knowledge Graph Embedding Explained

, 2022
In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec.Comment: Notes for subsampling methods in Knowledge Graph ...
Kamigaito, Hidetaka, Hayashi, Katsuhiko
core  

Knowledge Graph Embedding

, 2017
A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis ...
Hailun Lin, Yantao Jia, Xueqi Cheng, Yuanzhuo Wang, Xiaolong Jin   +4 more
core   +1 more source

Distance Based Korean WordNet(alias. KorLex) Embedding Model

Applied Artificial Intelligence
The objective of this study was to create graph embedding vectors using Korean WordNet (KorLex) and apply them to neural network word-embedding models.
SeongReol Park, JoongMin Shin, Sanghyun Cho, Hyuk-Chul Kwon, Jung-Hun Lee   +4 more
doaj   +1 more source

STKE: Temporal Knowledge Graph Embedding in the Spherical Coordinate System [PDF]

, 2022
Knowledge graph embedding (KGE) aims to learn the representation of entities and predicates in low-dimensional vector spaces which can complete the missing parts of the Knowledge Graphs (KGs).
Shen, Linshan, Liu, Ruinan, Wang, Shibo, Khattak, Asad Masood, Shibo Wang, Ruinan Liu, Linshan Shen, Asad Masood Khattak   +7 more
core   +1 more source