Knowledge Graphs have been widely used to represent facts in a structured
format. Due to their large scale applications, knowledge graphs suffer from
being incomplete. The relation prediction task obtains knowledge graph
completion by assigning one or more possible relations to each pair of nodes.
In this work, we make use of the knowledge graph node names to fine-tune a
large language model for the relation prediction task. By utilizing the node
names only we enable our model to operate sufficiently in the inductive
settings. Our experiments show that we accomplish new scores on a widely used
knowledge graph benchmark.

通过利用知识图谱节点名称，我们对大型语言模型进行了微调以进行关系预测任务，实验证明我们在广泛使用的知识图谱基准测试中取得了新的成绩。

利用大型语言模型进行知识图谱补全的关系预测

Relations Prediction for Knowledge Graph Completion using Large Language  Models

Knowledge graphs (KGs) are inherently incomplete because of incomplete world
knowledge and bias in what is the input to the KG. Additionally, world
knowledge constantly expands and evolves, making existing facts deprecated or
introducing new ones. However, we would still want to be able to answer queries
as if the graph were complete. In this chapter, we will give an overview of
several methods which have been proposed to answer queries in such a setting.
We will first provide an overview of the different query types which can be
supported by these methods and datasets typically used for evaluation, as well
as an insight into their limitations. Then, we give an overview of the
different approaches and describe them in terms of expressiveness, supported
graph types, and inference capabilities.

知识图谱是不完全的，因为世界知识不完整且受到输入的偏见影响。本文概述了几种方法，这些方法被提出来以在这种环境下回答查询。首先，我们提供了各种查询类型的概述，这些方法可以支持这些查询类型，并介绍了用于评估的数据集以及它们的局限性。然后，我们概述了不同的方法，并描述了它们的表达能力、支持的图类型和推理能力。

图查询的近似答案

Approximate Answering of Graph Queries

Point-cloud data collected in real-world applications are often incomplete.
Data is typically missing due to objects being observed from partial
viewpoints, which only capture a specific perspective or angle. Additionally,
data can be incomplete due to occlusion and low-resolution sampling. Existing
completion approaches rely on datasets of predefined objects to guide the
completion of noisy and incomplete, point clouds. However, these approaches
perform poorly when tested on Out-Of-Distribution (OOD) objects, that are
poorly represented in the training dataset. Here we leverage recent advances in
text-guided image generation, which lead to major breakthroughs in text-guided
shape generation. We describe an approach called SDS-Complete that uses a
pre-trained text-to-image diffusion model and leverages the text semantics of a
given incomplete point cloud of an object, to obtain a complete surface
representation. SDS-Complete can complete a variety of objects using test-time
optimization without expensive collection of 3D information. We evaluate SDS
Complete on incomplete scanned objects, captured by real-world depth sensors
and LiDAR scanners. We find that it effectively reconstructs objects that are
absent from common datasets, reducing Chamfer loss by 50% on average compared
with current methods. Project page: this https URL

本文提出了一种名为 SDS-Complete 的方法，该方法利用预训练的文本到图像扩散模型，借助给定不完整点云对象的文本语义获取完整的表面表示，其可以有效地重建通过现实世界深度传感器和 LiDAR 扫描仪捕获的不完整扫描物体，相较于当前方法平均减少了 50％的 Chamfer 损失。

使用预训练文本到图像扩散模型完成点云

Point-Cloud Completion with Pretrained Text-to-image Diffusion Models

Knowledge graphs (KGs) are typically incomplete and we often wish to infer
new facts given the existing ones. This can be thought of as a binary
classification problem; we aim to predict if new facts are true or false.
Unfortunately, we generally only have positive examples (the known facts) but
we also need negative ones to train a classifier. To resolve this, it is usual
to generate negative examples using a negative sampling strategy. However, this
can produce false negatives which may reduce performance, is computationally
expensive, and does not produce calibrated classification probabilities. In
this paper, we propose a training procedure that obviates the need for negative
sampling by adding a novel regularization term to the loss function. Our
results for two relational embedding models (DistMult and SimplE) show the
merit of our proposal both in terms of performance and speed.

该论文提出了一种训练过程，通过向损失函数添加新颖的正则化项，避免了使用负采样的需要，并且在两个关系嵌入模型（DistMult 和 SimplE）中得到了优异的性能和速度。