The knowledge graph (KG) that represents structural relations among entities has become
an increasingly important research field for knowledge-driven artificial intelligence. In this …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

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 completion (KGC) has become a focus of attention across deep learning
community owing to its excellent contribution to numerous downstream tasks. Although …

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 …

Predicting personality traits based on online posts has emerged as an important task in
many fields such as social network analysis. One of the challenges of this task is assembling …

Abstract Knowledge graph as a research topic is increasingly popular to represent structural
relations between entities. Recent years have witnessed the release of various open-source …

Abstract Graph Attention Networks (GATs) have proven a promising model that takes
advantage of localized attention mechanism to perform knowledge representation learning …

Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs,
which is a significant subtask of KG completion. Despite of its progress, however, we …

Abstract Knowledge graph completion (KGC) aims to infer missing information in incomplete
knowledge graphs (KGs). Most previous works only consider the transductive scenario …