[PDF][PDF] Knowledge Graph Embedding: An Overview
Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …
Pairre: Knowledge graph embeddings via paired relation vectors
Distance based knowledge graph embedding methods show promising results on link
prediction task, on which two topics have been widely studied: one is the ability to handle …
prediction task, on which two topics have been widely studied: one is the ability to handle …
JointE: Jointly utilizing 1D and 2D convolution for knowledge graph embedding
Abstract Knowledge graph embedding is a popular method to predict missing links for
knowledge graphs by projecting entities and relations into continuous low-dimension …
knowledge graphs by projecting entities and relations into continuous low-dimension …
Knowledge graph embedding model with attention-based high-low level features interaction convolutional network
Abstract Knowledge graphs are sizeable graph-structured knowledge with both abstract and
concrete concepts in the form of entities and relations. Recently, convolutional neural …
concrete concepts in the form of entities and relations. Recently, convolutional neural …
An efficiency relation-specific graph transformation network for knowledge graph representation learning
Abstract Knowledge graph representation learning (KGRL) aims to infer the missing links
between target entities based on existing triples. Graph neural networks (GNNs) have been …
between target entities based on existing triples. Graph neural networks (GNNs) have been …
Multi-level interaction based knowledge graph completion
With the continuous emergence of new knowledge, Knowledge Graph (KG) typically suffers
from the incompleteness problem, hindering the performance of downstream applications …
from the incompleteness problem, hindering the performance of downstream applications …
KDRank: Knowledge-driven user-aware POI recommendation
Accurate user modeling is crucial for point-of-interest (POI) recommendation as it can
significantly improve user satisfaction with recommended POIs and enrich user experience …
significantly improve user satisfaction with recommended POIs and enrich user experience …
Knowledge graph embedding with atrous convolution and residual learning
F Ren, J Li, H Zhang, S Liu, B Li, R Ming… - arXiv preprint arXiv …, 2020 - arxiv.org
Knowledge graph embedding is an important task and it will benefit lots of downstream
applications. Currently, deep neural networks based methods achieve state-of-the-art …
applications. Currently, deep neural networks based methods achieve state-of-the-art …
Cross-domain knowledge graph chiasmal embedding for multi-domain item-item recommendation
Recommender system can provide users with the required information accurately and
efficiently, playing a very important role in improving users' life experience. Although …
efficiently, playing a very important role in improving users' life experience. Although …
Triplere: Knowledge graph embeddings via tripled relation vectors
L Yu, Z Luo, H Liu, D Lin, H Li, Y Deng - arXiv preprint arXiv:2209.08271, 2022 - arxiv.org
Translation-based knowledge graph embedding has been one of the most important
branches for knowledge representation learning since TransE came out. Although many …
branches for knowledge representation learning since TransE came out. Although many …