In this paper we introduce TWIG (Topologically-Weighted Intelligence
Generation), a novel, embedding-free paradigm for simulating the output of KGEs
that uses a tiny fraction of the parameters. TWIG learns weights from inputs
that consist of topological features of the graph data, with no coding for
latent representations of entities or edges. Our experiments on the UMLS
dataset show that a single TWIG neural network can predict the results of
state-of-the-art ComplEx-N3 KGE model nearly exactly on across all
hyperparameter configurations. To do this it uses a total of 2590 learnable
parameters, but accurately predicts the results of 1215 different
hyperparameter combinations with a combined cost of 29,322,000 parameters.
Based on these results, we make two claims: 1) that KGEs do not learn latent
semantics, but only latent representations of structural patterns; 2) that
hyperparameter choice in KGEs is a deterministic function of the KGE model and
graph structure. We further hypothesise that, as TWIG can simulate KGEs without
embeddings, that node and edge embeddings are not needed to learn to accurately
predict new facts in KGs. Finally, we formulate all of our findings under the
umbrella of the ``Structural Generalisation Hypothesis", which suggests that
``twiggy" embedding-free / data-structure-based learning methods can allow a
single neural network to simulate KGE performance, and perhaps solve the Link
Prediction task, across many KGs from diverse domains and with different
semantics.

我们介绍了一种新颖的无嵌入方法 TWIG (Topologically-Weighted Intelligence Generation)，该方法使用极少数量的参数模拟知识图谱嵌入模型的输出结果，并从图数据的拓扑特征中学习权重，而无需对实体或边缘进行编码。在 UMLS 数据集上的实验证明，单个 TWIG 神经网络能够几乎精确地预测所有超参数配置下的最先进 ComplEx-N3 KGE 模型的结果。根据这些结果，我们提出两个观点：1）KGEs 不仅学习结构模式的潜在表示，而且只学习结构模式的潜在表示；2）KGEs 中的超参数选择是 KGE 模型和图结构的确定性函数。此外，我们假设由于 TWIG 能够在没有嵌入的情况下模拟 KGEs，因此节点和边嵌入不是学习准确预测知识图谱中新事实所必需的。最后，我们将所有发现都归结为 “结构概括假设”，该假设认为 “枝状” 无嵌入 / 数据结构导向的学习方法可以使单个神经网络在许多来自不同领域并具有不同语义的知识图谱上模拟 KGE 性能，并可能解决链接预测任务。

TWIG: 通过模拟 KGE 模型实现预处理超参数优化和跨图泛化

TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph  Generalisation via Simulated KGE Models

The autonomous driving (AD) industry is exploring the use of knowledge graphs
(KGs) to manage the vast amount of heterogeneous data generated from vehicular
sensors. The various types of equipped sensors include video, LIDAR and RADAR.
Scene understanding is an important topic in AD which requires consideration of
various aspects of a scene, such as detected objects, events, time and
location. Recent work on knowledge graph embeddings (KGEs) - an approach that
facilitates neuro-symbolic fusion - has shown to improve the predictive
performance of machine learning models. With the expectation that
neuro-symbolic fusion through KGEs will improve scene understanding, this
research explores the generation and evaluation of KGEs for autonomous driving
data. We also present an investigation of the relationship between the level of
informational detail in a KG and the quality of its derivative embeddings. By
systematically evaluating KGEs along four dimensions -- i.e. quality metrics,
KG informational detail, algorithms, and datasets -- we show that (1) higher
levels of informational detail in KGs lead to higher quality embeddings, (2)
type and relation semantics are better captured by the semantic transitional
distance-based TransE algorithm, and (3) some metrics, such as coherence
measure, may not be suitable for intrinsically evaluating KGEs in this domain.
Additionally, we also present an (early) investigation of the usefulness of
KGEs for two use-cases in the AD domain.

该研究旨在探讨自动驾驶数据的知识图嵌入生成和评估，并考虑知识图的信息细节与衍生嵌入质量的关系，结果表明，在自动驾驶领域，知识图的信息细节越高，衍生嵌入的质量越高，并且基于语义转移距离的 TransE 算法更好地捕捉类型和关系的语义。