Understanding logical entailments derived by a description logic reasoner is
not always straight-forward for ontology users. For this reason, various
methods for explaining entailments using justifications and proofs have been
developed and implemented as plug-ins for the ontology editor Prot\'eg\'e.
However, when the user expects a missing consequence to hold, it is equally
important to explain why it does not follow from the ontology. In this paper,
we describe a new version of $\rm E{\scriptsize VEE}$, a Prot\'eg\'e plugin
that now also provides explanations for missing consequences, via existing and
new techniques based on abduction and counterexamples.

描述逻辑推理器导出的逻辑蕴涵对本体用户来说并不总是直接明了。因此，我们描述了一个新版本的 Protégé 插件 EVee，通过基于引导和反例的现有和新技术，为缺失的结果提供解释。

为什么不呢？用 EVE 解释丢失的蕴涵（技术报告）

Why Not? Explaining Missing Entailments with $\rm E{\scriptsize VEE}$  (Technical Report)

We introduce a new dataset of logical entailments for the purpose of
measuring models' ability to capture and exploit the structure of logical
expressions against an entailment prediction task. We use this task to compare
a series of architectures which are ubiquitous in the sequence-processing
literature, in addition to a new model class---PossibleWorldNets---which
computes entailment as a "convolution over possible worlds". Results show that
convolutional networks present the wrong inductive bias for this class of
problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM
RNNs due to their enhanced ability to exploit the syntax of logic, and
PossibleWorldNets outperform all benchmarks.

介绍了一个新的逻辑蕴涵的数据集，用于测量模型在逻辑表达式的结构方面捕捉和利用的能力，比较了多个体系结构以及一个新的模型类 PossibleWorldNets，并表明卷积网络在这类问题上呈现了错误的归纳偏差，相对于 LSTM RNNs，树状神经网络由于其增强了利用逻辑语法的能力而优于 LSTM RNNs，而 PossibleWorldNets 优于所有基准。