Few-shot Knowledge Graph (KG) Relational Reasoning aims to predict unseen
triplets (i.e., query triplets) for rare relations in KGs, given only several
triplets of these relations as references (i.e., support triplets). This task
has gained significant traction due to the widespread use of knowledge graphs
in various natural language processing applications. Previous approaches have
utilized meta-training methods and manually constructed meta-relation sets to
tackle this task. Recent efforts have focused on edge-mask-based methods, which
exploit the structure of the contextualized graphs of target triplets (i.e., a
subgraph containing relevant triplets in the KG). However, existing
edge-mask-based methods have limitations in extracting insufficient information
from KG and are highly influenced by spurious information in KG. To overcome
these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational
Reasoning), a novel approach that effectively adapts the information in
contextualized graphs to various subgraphs generated from support and query
triplets to perform the prediction. Specifically, SAFER enables the extraction
of more comprehensive information from support triplets while minimizing the
impact of spurious information when predicting query triplets. Experimental
results on three prevalent datasets demonstrate the superiority of our proposed
framework SAFER.

Few-shot Knowledge Graph Relational Reasoning has become increasingly important in natural language processing applications, and SAFER presents a novel approach to effectively adapt contextualized graph information to perform prediction, demonstrating its superiority in experimental results.

基于子图适应的少样本知识图谱关系推理

Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation

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-based EA methods inherit the inborn defects from
their neural network lineage: weak scalability and poor interpretability.
Inspired by recent studies, we reinvent the Label Propagation algorithm to
effectively run on KGs and propose a non-neural EA framework -- LightEA,
consisting of three efficient components: (i) Random Orthogonal Label
Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn
Iteration. According to the extensive experiments on public datasets, LightEA
has impressive scalability, robustness, and interpretability. With a mere tenth
of time consumption, LightEA achieves comparable results to state-of-the-art
methods across all datasets and even surpasses them on many.

通过对研究发现，我们在本文中提出了一种非神经网络 EA 框架 LightEA，它包含了 Random Orthogonal Label Generation，Three-view Label Propagation 和 Sparse Sinkhorn Iteration 三个组件，从而达到极佳的可伸缩性、鲁棒性和可解释性，对多个公共数据集的实验证明了其与现有最先进方法可媲美甚至更好的结果。

LightEA: 基于三视图标签传播的可扩展、鲁棒和可解释的实体对齐框架

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

Knowledge Graphs (KGs) are composed of structured information about a
particular domain in the form of entities and relations. In addition to the
structured information KGs help in facilitating interconnectivity and
interoperability between different resources represented in the Linked Data
Cloud. KGs have been used in a variety of applications such as entity linking,
question answering, recommender systems, etc. However, KG applications suffer
from high computational and storage costs. Hence, there arises the necessity
for a representation able to map the high dimensional KGs into low dimensional
spaces, i.e., embedding space, preserving structural as well as relational
information. This paper conducts a survey of KG embedding models which not only
consider the structured information contained in the form of entities and
relations in a KG but also the unstructured information represented as literals
such as text, numerical values, images, etc. Along with a theoretical analysis
and comparison of the methods proposed so far for generating KG embeddings with
literals, an empirical evaluation of the different methods under identical
settings has been performed for the general task of link prediction.

本文对考虑包括文本、数字、图像等非结构化信息的 KG 嵌入模型进行了综述和对比，同时在相同条件下对链接预测的不同方法进行了实证评估。

知识图谱嵌入与文字字面量的调查：哪种模型更好地链接字面量？

A Survey on Knowledge Graph Embeddings with Literals: Which model links  better Literal-ly?

Knowledge graphs (KGs) are the key components of various natural language
processing applications. To further expand KGs' coverage, previous studies on
knowledge graph completion usually require a large number of training instances
for each relation. However, we observe that long-tail relations are actually
more common in KGs and those newly added relations often do not have many known
triples for training. In this work, we aim at predicting new facts under a
challenging setting where only one training instance is available. We propose a
one-shot relational learning framework, which utilizes the knowledge extracted
by embedding models and learns a matching metric by considering both the
learned embeddings and one-hop graph structures. Empirically, our model yields
considerable performance improvements over existing embedding models, and also
eliminates the need of re-training the embedding models when dealing with newly
added relations.

本文提出了一种基于嵌入模型的一次性关系学习框架，将已有的知识和一跳图结构相结合，来预测在只有一个训练样例的情况下的新事实，并通过实验证明，在新关系预测和嵌入模型训练上，该模型具有巨大的性能优势。