The task of entity alignment between knowledge graphs (KGs) aims to identify
every pair of entities from two different KGs that represent the same entity.
Many machine learning-based methods have been proposed for this task. However,
to our best knowledge, existing methods all require manually crafted seed
alignments, which are expensive to obtain. In this paper, we propose the first
fully automatic alignment method named AutoAlign, which does not require any
manually crafted seed alignments. Specifically, for predicate embeddings,
AutoAlign constructs a predicate-proximity-graph with the help of large
language models to automatically capture the similarity between predicates
across two KGs. For entity embeddings, AutoAlign first computes the entity
embeddings of each KG independently using TransE, and then shifts the two KGs'
entity embeddings into the same vector space by computing the similarity
between entities based on their attributes. Thus, both predicate alignment and
entity alignment can be done without manually crafted seed alignments.
AutoAlign is not only fully automatic, but also highly effective. Experiments
using real-world KGs show that AutoAlign improves the performance of entity
alignment significantly compared to state-of-the-art methods.

这篇文章介绍了一种名为 AutoAlign 的全自动实体对齐方法，它利用大型语言模型构建谓词接近性图，通过计算实体属性的相似性将两个知识图谱的实体嵌入对齐到同一个向量空间，从而在没有手工标注种子对齐的情况下实现实体对齐，并且实验证明 AutoAlign 在实体对齐上相比于现有方法具有显著的性能提升。

AutoAlign: 全自动有效的知识图谱对齐

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment  enabled by Large Language Models

Object detection through either RGB images or the LiDAR point clouds has been
extensively explored in autonomous driving. However, it remains challenging to
make these two data sources complementary and beneficial to each other. In this
paper, we propose \textit{AutoAlign}, an automatic feature fusion strategy for
3D object detection. Instead of establishing deterministic correspondence with
camera projection matrix, we model the mapping relationship between the image
and point clouds with a learnable alignment map. This map enables our model to
automate the alignment of non-homogenous features in a dynamic and data-driven
manner. Specifically, a cross-attention feature alignment module is devised to
adaptively aggregate \textit{pixel-level} image features for each voxel. To
enhance the semantic consistency during feature alignment, we also design a
self-supervised cross-modal feature interaction module, through which the model
can learn feature aggregation with \textit{instance-level} feature guidance.
Extensive experimental results show that our approach can lead to 2.3 mAP and
7.0 mAP improvements on the KITTI and nuScenes datasets, respectively. Notably,
our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving
competitive performance among various state-of-the-arts.

本研究提出了一种自动特征融合策略 AutoAlign，通过可学习的对齐映射以及交叉注意力特征对齐模块和自监督跨模态特征交互模块实现图像和点云数据源的同步处理，实验结果表明该方法在 KITTI 和 nuScenes 数据集上都有较好的表现。