Large language models (LLMs) have shown significant achievements in solving a
wide range of tasks. Recently, LLMs' capability to store, retrieve and infer
with symbolic knowledge has drawn a great deal of attention, showing their
potential to understand structured information. However, it is not yet known
whether LLMs can understand Description Logic (DL) ontologies. In this work, we
empirically analyze the LLMs' capability of understanding DL-Lite ontologies
covering 6 representative tasks from syntactic and semantic aspects. With
extensive experiments, we demonstrate both the effectiveness and limitations of
LLMs in understanding DL-Lite ontologies. We find that LLMs can understand
formal syntax and model-theoretic semantics of concepts and roles. However,
LLMs struggle with understanding TBox NI transitivity and handling ontologies
with large ABoxes. We hope that our experiments and analyses provide more
insights into LLMs and inspire to build more faithful knowledge engineering
solutions.

经验分析表明，大型语言模型可以理解 DL-Lite 本体论，但在处理具有大型 ABox 的本体论以及理解 TBox NI 传递性方面存在困难。

大型语言模型是否能理解 DL-Lite 本体？一项实证研究

Can Large Language Models Understand DL-Lite Ontologies? An Empirical  Study

When working with description logic ontologies, understanding entailments
derived by a description logic reasoner is not always straightforward. So far,
the standard ontology editor Protégé offers two services to help:
(black-box) justifications for OWL 2 DL ontologies, and (glass-box) proofs for
lightweight OWL EL ontologies, where the latter exploits the proof facilities
of reasoner ELK. Since justifications are often insufficient in explaining
inferences, there is thus only little tool support for explaining inferences in
more expressive DLs. In this paper, we introduce EVEE-LIBS, a Java library for
computing proofs for DLs up to ALCH, and EVEE-PROTEGE, a collection of
Protégé plugins for displaying those proofs in Protégé. We also give a
short glimpse of the latest version of EVONNE, a more advanced standalone
application for displaying and interacting with proofs computed with EVEE-LIBS.

本文介绍了一个计算 DLs 证明的 Java 库及其在 Protégé 中的插件，并提供了一款显示计算结果的高级应用。

OWL 本体真正可解释性的前夕：采用 Evee 和 Evonne 的描述逻辑证明（扩展版）

On the Eve of True Explainability for OWL Ontologies: Description Logic Proofs with Evee and Evonne (Extended Version)

The quest for acquiring a formal representation of the knowledge of a domain
of interest has attracted researchers with various backgrounds into a diverse
field called ontology learning. We highlight classical machine learning and
data mining approaches that have been proposed for (semi-)automating the
creation of description logic (DL) ontologies. These are based on association
rule mining, formal concept analysis, inductive logic programming,
computational learning theory, and neural networks. We provide an overview of
each approach and how it has been adapted for dealing with DL ontologies.
Finally, we discuss the benefits and limitations of each of them for learning
DL ontologies.

该文介绍了应用于形式化知识表示领域的本体学习中的经典机器学习和数据挖掘方法，包括关联规则挖掘、正式概念分析、归纳逻辑编程、计算学习理论及神经网络，总结了各种方法在学习 DL 本体方面的优点和限制。