While many methods for learning vector space embeddings have been proposed in
the field of Natural Language Processing, these methods typically do not
distinguish between categories and individuals. Intuitively, if individuals are
represented as vectors, we can think of categories as (soft) regions in the
embedding space. Unfortunately, meaningful regions can be difficult to
estimate, especially since we often have few examples of individuals that
belong to a given category. To address this issue, we rely on the fact that
different categories are often highly interdependent. In particular, categories
often have conceptual neighbors, which are disjoint from but closely related to
the given category (e.g.\ fruit and vegetable). Our hypothesis is that more
accurate category representations can be learned by relying on the assumption
that the regions representing such conceptual neighbors should be adjacent in
the embedding space. We propose a simple method for identifying conceptual
neighbors and then show that incorporating these conceptual neighbors indeed
leads to more accurate region based representations.

本文介绍了一种将概念邻居纳入考虑的方法来学习区域向量表示，从而得到更加准确的基于区域的表示。

利用概念邻域对语义类别建模

Modelling Semantic Categories using Conceptual Neighborhood

Recent years have witnessed the successful application of low-dimensional
vector space representations of knowledge graphs to predict missing facts or
find erroneous ones. However, it is not yet well-understood to what extent
ontological knowledge, e.g. given as a set of (existential) rules, can be
embedded in a principled way. To address this shortcoming, in this paper we
introduce a general framework based on a view of relations as regions, which
allows us to study the compatibility between ontological knowledge and
different types of vector space embeddings. Our technical contribution is
two-fold. First, we show that some of the most popular existing embedding
methods are not capable of modelling even very simple types of rules, which in
particular also means that they are not able to learn the type of dependencies
captured by such rules. Second, we study a model in which relations are
modelled as convex regions. We show particular that ontologies which are
expressed using so-called quasi-chained existential rules can be exactly
represented using convex regions, such that any set of facts which is induced
using that vector space embedding is logically consistent and deductively
closed with respect to the input ontology.

本文提出了一种基于关系视图的框架，使我们可以研究本体知识和不同类型向量空间嵌入之间的兼容性，并证明了一些现有的嵌入方法无法建模一些简单的规则，同时研究了关系建模为凸区域的模型，发现使用凸区域可精确表示所谓的准链式存在量规则的本体，从而对于所有在这个向量空间内运用的事实都是逻辑一致和推导闭合的。

从知识图谱嵌入到本体嵌入？向量空间表示和规则之间的兼容性分析

From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the  Compatibility between Vector Space Representations and Rules

An important challenge for human-like AI is compositional semantics. Recent
research has attempted to address this by using deep neural networks to learn
vector space embeddings of sentences, which then serve as input to other tasks.
We present a new dataset for one such task, `natural language inference' (NLI),
that cannot be solved using only word-level knowledge and requires some
compositionality. We find that the performance of state of the art sentence
embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We
analyze the decision rules learned by InferSent and find that they are
consistent with simple heuristics that are ecologically valid in its training
dataset. Further, we find that augmenting training with our dataset improves
test performance on our dataset without loss of performance on the original
training dataset. This highlights the importance of structured datasets in
better understanding and improving AI systems.

本文介绍了一个新的自然语言推理任务数据集，旨在解决人工智能中的句子组成意思的问题，使用深度神经网络学习语义向量表示法，得出发现表明 Inference 等现有句向量算法性能较差，但使用本文提供的数据集进行训练，可提高测试性能和提高人工智能系统的表现水平。