Knowledge graph (KG) embedding is a fundamental task in natural language
processing, and various methods have been proposed to explore semantic patterns
in distinctive ways. In this paper, we propose to learn an ensemble by
leveraging existing methods in a relation-aware manner. However, exploring
these semantics using relation-aware ensemble leads to a much larger search
space than general ensemble methods. To address this issue, we propose a
divide-search-combine algorithm RelEns-DSC that searches the relation-wise
ensemble weights independently. This algorithm has the same computation cost as
general ensemble methods but with much better performance. Experimental results
on benchmark datasets demonstrate the effectiveness of the proposed method in
efficiently searching relation-aware ensemble weights and achieving
state-of-the-art embedding performance. The code is public at
this https URL

通过关系感知的集成学习算法，提取知识图谱嵌入的语义模式，以达到更好的性能和效果。

关系感知的集成学习用于知识图谱嵌入

Relation-aware Ensemble Learning for Knowledge Graph Embedding

This paper proposes a novel model for recognizing images with composite
attribute-object concepts, notably for composite concepts that are unseen
during model training. We aim to explore the three key properties required by
the task --- relation-aware, consistent, and decoupled --- to learn rich and
robust features for primitive concepts that compose attribute-object pairs. To
this end, we propose the Blocked Message Passing Network (BMP-Net). The model
consists of two modules. The concept module generates semantically meaningful
features for primitive concepts, whereas the visual module extracts visual
features for attributes and objects from input images. A message passing
mechanism is used in the concept module to capture the relations between
primitive concepts. Furthermore, to prevent the model from being biased towards
seen composite concepts and reduce the entanglement between attributes and
objects, we propose a blocking mechanism that equalizes the information
available to the model for both seen and unseen concepts. Extensive experiments
and ablation studies on two benchmarks show the efficacy of the proposed model.

本文提出了一种模型，使用基于阻塞消息传递网络的方法来识别由属性和对象组成的复合概念的图像，特别是用于未经过模型训练的复合概念。实验证明该模型是具有关系感知、一致性和分离三个关键属性的，利用这些属性学习了由基本概念组成的丰富而强健的特征。

基于关系感知的组合式零样本学习用于属性 - 物品对识别

Relation-aware Compositional Zero-shot Learning for Attribute-Object  Pair Recognition

For person re-identification (re-id), attention mechanisms have become
attractive as they aim at strengthening discriminative features and suppressing
irrelevant ones, which matches well the key of re-id, i.e., discriminative
feature learning. Previous approaches typically learn attention using local
convolutions, ignoring the mining of knowledge from global structure patterns.
Intuitively, the affinities among spatial positions/nodes in the feature map
provide clustering-like information and are helpful for inferring semantics and
thus attention, especially for person images where the feasible human poses are
constrained. In this work, we propose an effective Relation-Aware Global
Attention (RGA) module which captures the global structural information for
better attention learning. Specifically, for each feature position, in order to
compactly grasp the structural information of global scope and local appearance
information, we propose to stack the relations, i.e., its pairwise
correlations/affinities with all the feature positions (e.g., in raster scan
order), and the feature itself together to learn the attention with a shallow
convolutional model. Extensive ablation studies demonstrate that our RGA can
significantly enhance the feature representation power and help achieve the
state-of-the-art performance on several popular benchmarks. The source code is
available at
this https URL

本研究提出了一种关系感知全局注意力 (RGA) 模块，可以全面抓取全局结构信息以增强特征表示能力，实现了人员复识别 (Re-id) 领域的最新技术成果.