Analogical inference is a remarkable capability of human reasoning, and has
been used to solve hard reasoning tasks. Analogy based reasoning (AR) has
gained increasing interest from the artificial intelligence community and has
shown its potential in multiple machine learning tasks such as classification,
decision making and recommendation with competitive results. We propose a deep
learning (DL) framework to address and tackle two key tasks in AR: analogy
detection and solving. The framework is thoroughly tested on the Siganalogies
dataset of morphological analogical proportions (APs) between words, and shown
to outperform symbolic approaches in many languages. Previous work have
explored the behavior of the Analogy Neural Network for classification (ANNc)
on analogy detection and of the Analogy Neural Network for retrieval (ANNr) on
analogy solving by retrieval, as well as the potential of an autoencoder (AE)
for analogy solving by generating the solution word. In this article we
summarize these findings and we extend them by combining ANNr and the AE
embedding model, and checking the performance of ANNc as an retrieval method.
The combination of ANNr and AE outperforms the other approaches in almost all
cases, and ANNc as a retrieval method achieves competitive or better
performance than 3CosMul. We conclude with general guidelines on using our
framework to tackle APs with DL.

本文提出了一个深度学习框架来解决类比检测和解决两个关键问题，并在 morphological analogical proportions 数据集上进行了测试表明优于符号方法。文章总结了之前的研究，并结合了 ANNe 和 AE 嵌入模型。最终得出该模型在绝大多数情况下都优于其他方法，并提供了使用 DL 来处理类比问题的一般指南。

解决形态学类比问题：从检索到生成

Solving morphological analogies: from retrieval to generation

Knowledge graph embedding (KGE), which maps entities and relations in a
knowledge graph into continuous vector spaces, has achieved great success in
predicting missing links in knowledge graphs. However, knowledge graphs often
contain incomplete triples that are difficult to inductively infer by KGEs. To
address this challenge, we resort to analogical inference and propose a novel
and general self-supervised framework AnKGE to enhance KGE models with
analogical inference capability. We propose an analogical object retriever that
retrieves appropriate analogical objects from entity-level, relation-level, and
triple-level. And in AnKGE, we train an analogy function for each level of
analogical inference with the original element embedding from a well-trained
KGE model as input, which outputs the analogical object embedding. In order to
combine inductive inference capability from the original KGE model and
analogical inference capability enhanced by AnKGE, we interpolate the analogy
score with the base model score and introduce the adaptive weights in the score
function for prediction. Through extensive experiments on FB15k-237 and WN18RR
datasets, we show that AnKGE achieves competitive results on link prediction
task and well performs analogical inference.

本研究提出了一个自监督增强的框架 AnKGE，其中包括不同层次的类比推理来增强知识图谱嵌入 (KGE) 模型，以预测缺失的关系。在知识图谱不完整的情况下，AnKGE 结合了归纳学习和类比推理的方法，在 FB15k-237 和 WN18RR 数据集上实现了竞争性结果。

模拟推理增强的知识图谱嵌入

Analogical Inference Enhanced Knowledge Graph Embedding

Analogical proportions are 4-ary relations that read "A is to B as C is to
D". Recent works have highlighted the fact that such relations can support a
specific form of inference, called analogical inference. This inference
mechanism was empirically proved to be efficient in several reasoning and
classification tasks. In the latter case, it relies on the notion of analogy
preservation.
In this paper, we explore this relation between formal models of analogy and
the corresponding classes of analogy preserving functions, and we establish a
Galois theory of analogical classifiers. We illustrate the usefulness of this
Galois framework over Boolean domains, and we explicitly determine the closed
sets of analogical classifiers, i.e., classifiers that are compatible with the
analogical inference, for each pair of Boolean analogies.

通过建立类比保持函数的形式模型和相应的类比模型之间的关系，我们建立了一个类比分类器的 Galois 理论并确定了每对布尔类比的类比分类器的闭合集。