Apr 25, 2022 · Despite achieving remarkable performance, prevailing graph alignment models still suffer from noisy supervision, yet how to mitigate the impact ...
Duration: 17:05
Posted: Sep 12, 2022
Posted: Sep 12, 2022
Afterward, a series of methods were proposed to integrate knowledge from different modalities (e.g., relational, visual, and numerical) to obtain joint ...
In this paper, we study a novel and widely existing prob- lem in graph matching (GM), namely, Bi-level Noisy Cor- respondence (BNC), which refers to ...
Jan 1, 2022 · To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which ...
However, both techniques are sufficient to support the noisy ... Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model.
Apr 6, 2024 · To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique ...
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion.
alignment frameworks often require supervision data in the form of a set of ... 5: Robustness of graph alignment models against noise on EN-DE-V2 test set.
May 22, 2022 · Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise ...