Concept learning approaches based on refinement operators explore partially
ordered solution spaces to compute concepts, which are used as binary
classification models for individuals. However, the number of concepts explored
by these approaches can grow to the millions for complex learning problems.
This often leads to impractical runtimes. We propose to alleviate this problem
by predicting the length of target concepts before the exploration of the
solution space. By these means, we can prune the search space during concept
learning. To achieve this goal, we compare four neural architectures and
evaluate them on four benchmarks. Our evaluation results suggest that recurrent
neural network architectures perform best at concept length prediction with a
macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm,
which learns ALC concepts, with our concept length predictor. Our extension
yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times
faster than other state-of-the-art concept learning algorithms for ALC --
including CELOE -- and achieves significant improvements in the F-measure of
the concepts learned on 3 out of 4 datasets. For reproducibility, we provide
our implementation in the public GitHub repository at
this https URL

通过神经网络进行概念长度预测，优化基于细化算子的概念学习方法，提出 CLIP 算法，可实现 ALC 概念学习的有效加速，且精度得到提升。

学习概念长度加速 ALC 中的概念学习

Learning Concept Lengths Accelerates Concept Learning in ALC

Ontology engineering is a hard and error-prone task, in which small changes
may lead to errors, or even produce an inconsistent ontology. As ontologies
grow in size, the need for automated methods for repairing inconsistencies
while preserving as much of the original knowledge as possible increases. Most
previous approaches to this task are based on removing a few axioms from the
ontology to regain consistency. We propose a new method based on weakening
these axioms to make them less restrictive, employing the use of refinement
operators. We introduce the theoretical framework for weakening DL ontologies,
propose algorithms to repair ontologies based on the framework, and provide an
analysis of the computational complexity. Through an empirical analysis made
over real-life ontologies, we show that our approach preserves significantly
more of the original knowledge of the ontology than removing axioms.

本研究提出了一种基于弱化 DL 本体学说公理，采用细化运算符修复基于原理的本体的新方法，研究表明该方法比删除公理保留原有知识更有效。