We propose bounded fitting as a scheme for learning description logic
concepts in the presence of ontologies. A main advantage is that the resulting
learning algorithms come with theoretical guarantees regarding their
generalization to unseen examples in the sense of PAC learning. We prove that,
in contrast, several other natural learning algorithms fail to provide such
guarantees. As a further contribution, we present the system SPELL which
efficiently implements bounded fitting for the description logic
$\mathcal{ELH}^r$ based on a SAT solver, and compare its performance to a
state-of-the-art learner.

提出有界拟合作为一种模式学习的方案，该方案可以在本体存在的情况下学习描述逻辑概念，并且称之为 PAC 学习的通用性具有理论保证。同时，我们提供了一种名为 SPELL 的系统，其基于 SAT 求解器高效实施了有界拟合，并将其表现与最先进的学习者进行了比较。

基于 SAT 的描述逻辑概念的 PAC 学习

SAT-Based PAC Learning of Description Logic Concepts

We explore the challenge of action prediction from textual descriptions of
scenes, a testbed to approximate whether text inference can be used to predict
upcoming actions. As a case of study, we consider the world of the Harry Potter
fantasy novels and inferring what spell will be cast next given a fragment of a
story. Spells act as keywords that abstract actions (e.g. 'Alohomora' to open a
door) and denote a response to the environment. This idea is used to
automatically build HPAC, a corpus containing 82,836 samples and 85 actions. We
then evaluate different baselines. Among the tested models, an LSTM-based
approach obtains the best performance for frequent actions and large scene
descriptions, but approaches such as logistic regression behave well on
infrequent actions.

本文讨论了利用文本描述来预测活动的挑战，以哈利・波特系列小说中的咒语作为抽象活动的关键词，并使用 LSTM 方法来预测下一个咒语。