A comprehensive overview of knowledge graph completion
T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …
downstream knowledge-aware tasks (such as recommendation and intelligent question …
Graph convolutional networks in language and vision: A survey
Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …
and have achieved good performance in a range of applications, including social …
Bi-CLKT: Bi-graph contrastive learning based knowledge tracing
Abstract The goal of Knowledge Tracing (KT) is to estimate how well students have
mastered a concept based on their historical learning of related exercises. The benefit of …
mastered a concept based on their historical learning of related exercises. The benefit of …
A survey on deep learning based knowledge tracing
Abstract “Knowledge tracing (KT)” is an emerging and popular research topic in the field of
online education that seeks to assess students' mastery of a concept based on their …
online education that seeks to assess students' mastery of a concept based on their …
Sentiment analysis and topic modeling for COVID-19 vaccine discussions
The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two
years and has had an unprecedented impact on the daily lives of people around the world …
years and has had an unprecedented impact on the daily lives of people around the world …
Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data
X Kong, W Zhou, G Shen, W Zhang, N Liu… - Knowledge-Based …, 2023 - Elsevier
In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …
sensors often exhibit missing or corrupted data, significantly hindering the development of …
[HTML][HTML] Graph transformer network with temporal kernel attention for skeleton-based action recognition
Skeleton-based human action recognition has caused wide concern, as skeleton data can
robustly adapt to dynamic circumstances such as camera view changes and background …
robustly adapt to dynamic circumstances such as camera view changes and background …
Collaborative representation learning for nodes and relations via heterogeneous graph neural network
Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly
suitable for characterizing real-world complex systems. In recent years, due to their strong …
suitable for characterizing real-world complex systems. In recent years, due to their strong …
Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision
Among existing knowledge graph based question answering (KGQA) methods, relation
supervision methods require labeled intermediate relations for stepwise reasoning. To avoid …
supervision methods require labeled intermediate relations for stepwise reasoning. To avoid …
Robust cross-network node classification via constrained graph mutual information
The recent methods for cross-network node classification mainly exploit graph neural
networks (GNNs) as feature extractor to learn expressive graph representations across the …
networks (GNNs) as feature extractor to learn expressive graph representations across the …