Methods for knowledge graph (KG) completion need to capture semantic
regularities and use these regularities to infer plausible knowledge that is
not explicitly stated. Most embedding-based methods are opaque in the kinds of
regularities they can capture, although region-based KG embedding models have
emerged as a more transparent alternative. By modeling relations as geometric
regions in high-dimensional vector spaces, such models can explicitly capture
semantic regularities in terms of the spatial arrangement of these regions.
Unfortunately, existing region-based approaches are severely limited in the
kinds of rules they can capture. We argue that this limitation arises because
the considered regions are defined as the Cartesian product of two-dimensional
regions. As an alternative, in this paper, we propose RESHUFFLE, a simple model
based on ordering constraints that can faithfully capture a much larger class
of rule bases than existing approaches. Moreover, the embeddings in our
framework can be learned by a monotonic Graph Neural Network (GNN), which
effectively acts as a differentiable rule base. This approach has the important
advantage that embeddings can be easily updated as new knowledge is added to
the KG. At the same time, since the resulting representations can be used
similarly to standard KG embeddings, our approach is significantly more
efficient than existing approaches to differentiable reasoning.

在这篇论文中，我们提出了一种基于顺序约束的简单模型 RESHUFFLE，能够比现有方法更好地捕捉规则，同时在我们的框架中嵌入可以通过可微分的图神经网络（GNN）进行学习，从而具有更高的效率。

利用基于区域的图神经网络进行知识图谱的可微分推理

Differentiable Reasoning about Knowledge Graphs with Region-based Graph  Neural Networks

Region based knowledge graph embeddings represent relations as geometric
regions. This has the advantage that the rules which are captured by the model
are made explicit, making it straightforward to incorporate prior knowledge and
to inspect learned models. Unfortunately, existing approaches are severely
restricted in their ability to model relational composition, and hence also
their ability to model rules, thus failing to deliver on the main promise of
region based models. With the aim of addressing these limitations, we
investigate regions which are composed of axis-aligned octagons. Such octagons
are particularly easy to work with, as intersections and compositions can be
straightforwardly computed, while they are still sufficiently expressive to
model arbitrary knowledge graphs. Among others, we also show that our octagon
embeddings can properly capture a non-trivial class of rule bases. Finally, we
show that our model achieves competitive experimental results.

基于地区的知识图谱嵌入将关系表示为几何区域，通过明确捕捉模型所涵盖的规则，能够简单地整合先前知识和检查学习模型。然而，现有方法在建模关系合成能力以及规则建模能力上存在严重限制，无法实现基于区域模型的主要承诺。为了解决这些局限性，我们研究了由轴对齐八边形组成的区域。这些八边形易于处理，因为交叉和组合可以直接计算，同时仍然足够表达任意知识图谱。此外，我们还表明我们的八边形嵌入可以正确捕捉一类非平凡的规则基，并且我们的模型取得了有竞争力的实验结果。

使用八角嵌入捕获知识图谱和规则

Capturing Knowledge Graphs and Rules with Octagon Embeddings

Efficient detection and description of geometric regions in images is a
prerequisite in visual systems for localization and mapping. Such systems still
rely on traditional hand-crafted methods for efficient generation of
lightweight descriptors, a common limitation of the more powerful neural
network models that come with high compute and specific hardware requirements.
In this paper, we focus on the adaptations required by detection and
description neural networks to enable their use in computationally limited
platforms such as robots, mobile, and augmented reality devices. To that end,
we investigate and adapt network quantization techniques to accelerate
inference and enable its use on compute limited platforms. In addition, we
revisit common practices in descriptor quantization and propose the use of a
binary descriptor normalization layer, enabling the generation of distinctive
binary descriptors with a constant number of ones. ZippyPoint, our efficient
quantized network with binary descriptors, improves the network runtime speed,
the descriptor matching speed, and the 3D model size, by at least an order of
magnitude when compared to full-precision counterparts. These improvements come
at a minor performance degradation as evaluated on the tasks of homography
estimation, visual localization, and map-free visual relocalization. Code and
models are available at this https URL

研究如何在计算能力有限的平台上使用检测和描述神经网络，探索和适应网络量化技术来提高推理速度，并建议使用二进制描述符规范化层，生成具有恒定数字的显著二进制描述符。通过 ZippyPoint，可以在与全精度模型相比，可显著提高网络运行速度、描述符匹配速度和 3D 模型大小的情况下增加一个数量级的性能。