GS-InGAT: An interaction graph attention network with global semantic for knowledge graph completion
Abstract Knowledge graph completion (KGC) aims to infer missing links between entities
based on the observed ones. Current KGC methods primarily focus on KG embedding …
based on the observed ones. Current KGC methods primarily focus on KG embedding …
Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism
H Zhang, L Bai - Neural Networks, 2023 - Elsevier
Few-shot knowledge graph completion (KGC) is an important and common task in real
applications, which aims to predict unseen facts when only few samples are available for …
applications, which aims to predict unseen facts when only few samples are available for …
Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph
H Cui, T Peng, R Han, B Zhu, H Bi, L Liu - Information Processing & …, 2023 - Elsevier
Text-enhanced and implicit reasoning methods are proposed for answering questions over
incomplete knowledge graph (KG), whereas prior studies either rely on external resources …
incomplete knowledge graph (KG), whereas prior studies either rely on external resources …
SARF: Aliasing Relation–Assisted Self-Supervised Learning for Few-Shot Relation Reasoning
Few-shot relation reasoning on knowledge graphs (FS-KGR) is an important and practical
problem that aims to infer long-tail relations and has drawn increasing attention these years …
problem that aims to infer long-tail relations and has drawn increasing attention these years …
Customized subgraph selection and encoding for drug-drug interaction prediction
Subgraph-based methods have proven to be effective and interpretable in predicting drug-
drug interactions (DDIs), which are essential for medical practice and drug development …
drug interactions (DDIs), which are essential for medical practice and drug development …
Contrast then memorize: Semantic neighbor retrieval-enhanced inductive multimodal knowledge graph completion
A large number of studies have emerged for Multimodal Knowledge Graph Completion
(MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed …
(MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed …
Kalm: Knowledge-aware integration of local, document, and global contexts for long document understanding
With the advent of pretrained language models (LMs), increasing research efforts have been
focusing on infusing commonsense and domain-specific knowledge to prepare LMs for …
focusing on infusing commonsense and domain-specific knowledge to prepare LMs for …
Supervised contrastive knowledge graph learning for ncRNA–disease association prediction
Y Wang, X **e, Y Wang, N Sheng, L Huang… - Expert Systems with …, 2025 - Elsevier
Accurately identifying potential disease-related non-coding RNAs (ncRNAs), including
circular RNAs (circRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), is …
circular RNAs (circRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), is …
A knowledge graph completion model based on triple level interaction and contrastive learning
J Hu, H Yang, F Teng, S Du, T Li - Pattern Recognition, 2024 - Elsevier
Abstract Knowledge graphs provide credible and structured knowledge for downstream
tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of …
tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of …
Multi-relational graph contrastive learning with learnable graph augmentation
Multi-relational graph learning aims to embed entities and relations in knowledge graphs
into low-dimensional representations, which has been successfully applied to various multi …
into low-dimensional representations, which has been successfully applied to various multi …