Knowledge graphs meet multi-modal learning: A comprehensive survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
A review of graph neural networks and pretrained language models for knowledge graph reasoning
J Ma, B Liu, K Li, C Li, F Zhang, X Luo, Y Qiao - Neurocomputing, 2024 - Elsevier
Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …
structure but faces challenges such as incomplete construction or inability to handle new …
Meaformer: Multi-modal entity alignment transformer for meta modality hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different
knowledge graphs (KGs) whose entities are associated with relevant images. However …
knowledge graphs (KGs) whose entities are associated with relevant images. However …
Unsupervised entity alignment for temporal knowledge graphs
Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities
between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend …
between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend …
Rethinking uncertainly missing and ambiguous visual modality in multi-modal entity alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …
Lightea: A scalable, robust, and interpretable entity alignment framework via three-view label propagation
Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core
step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN …
step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN …
Distribution-aware hybrid noise augmentation in graph contrastive learning for recommendation
The recommender systems are one of the most effective big data tools for solving the
information overload problem, but data sparsity greatly affects its performance. However …
information overload problem, but data sparsity greatly affects its performance. However …
Promptem: prompt-tuning for low-resource generalized entity matching
Entity Matching (EM), which aims to identify whether two entity records from two relational
tables refer to the same real-world entity, is one of the fundamental problems in data …
tables refer to the same real-world entity, is one of the fundamental problems in data …
TS-align: A temporal similarity-aware entity alignment model for temporal knowledge graphs
Z Zhang, L Bai, L Zhu - Information Fusion, 2024 - Elsevier
Entity Alignment (EA) is a crucial step in knowledge graph fusion, aiming to match
equivalent entity pairs across different knowledge graphs (KGs). In recent years, Temporal …
equivalent entity pairs across different knowledge graphs (KGs). In recent years, Temporal …
Real-time workload pattern analysis for large-scale cloud databases
Hosting database services on cloud systems has become a common practice. This has led
to the increasing volume of database workloads, which provides the opportunity for pattern …
to the increasing volume of database workloads, which provides the opportunity for pattern …