Despite the large-scale uptake of semantic technologies in the biomedical
domain, little is known about common modelling practices in published
ontologies. OWL ontologies are often published only in the crude form of sets
of axioms leaving the underlying design opaque. However, a principled and
systematic ontology development life cycle is likely to be reflected in
regularities of the ontology's emergent syntactic structure. To develop an
understanding of this emergent structure, we propose to reverse-engineer
ontologies taking a syntax-directed approach for identifying and analysing
regularities for axioms and sets of axioms. We survey BioPortal in terms of
syntactic modelling trends and common practices for OWL axioms and class
frames. Our findings suggest that biomedical ontologies only share simple
syntactic structures in which OWL constructors are not deeply nested or
combined in a complex manner. While such simple structures often account for
large proportions of axioms in a given ontology, many ontologies also contain
non-trivial amounts of more complex syntactic structures that are not common
across ontologies.

本研究基于对 BioPortal 的调查，提出一种基于语法的方法，用于识别和分析公共本体学建模实践，发现生物医学本体学只在 OWL 构造不深度嵌套或复杂组合时共享简单的句法结构，然而很多本体学也包含非常规的句法结构。

生物医学本体中的语法建模结构调研

A Survey of Syntactic Modelling Structures in Biomedical Ontologies

Semantic embedding of knowledge graphs has been widely studied and used for
prediction and statistical analysis tasks across various domains such as
Natural Language Processing and the Semantic Web. However, less attention has
been paid to developing robust methods for embedding OWL (Web Ontology
Language) ontologies which can express a much wider range of semantics than
knowledge graphs and have been widely adopted in domains such as
bioinformatics. In this paper, we propose a random walk and word embedding
based ontology embedding method named OWL2Vec*, which encodes the semantics of
an OWL ontology by taking into account its graph structure, lexical information
and logical constructors. Our empirical evaluation with three real world
datasets suggests that OWL2Vec* benefits from these three different aspects of
an ontology in class membership prediction and class subsumption prediction
tasks. Furthermore, OWL2Vec* often significantly outperforms the
state-of-the-art methods in our experiments.

本文提出了一种基于随机游走和词嵌入的本体嵌入方法 OWL2Vec *，该方法将 OWL 本体的语义编码为结合了图结构，词汇信息和逻辑构造式的表示。通过在三个真实数据集上的实证评估，我们发现 OWL2Vec * 在类成员预测和类包含预测任务中从本体的三个不同方面受益，并且通常在我们的实验中优于现有的技术水平。

OWL2Vec*: OWL 本体嵌入

OWL2Vec*: Embedding of OWL Ontologies

We present NaturalOWL, a natural language generation system that produces
texts describing individuals or classes of OWL ontologies. Unlike simpler OWL
verbalizers, which typically express a single axiom at a time in controlled,
often not entirely fluent natural language primarily for the benefit of domain
experts, we aim to generate fluent and coherent multi-sentence texts for
end-users. With a system like NaturalOWL, one can publish information in OWL on
the Web, along with automatically produced corresponding texts in multiple
languages, making the information accessible not only to computer programs and
domain experts, but also end-users. We discuss the processing stages of
NaturalOWL, the optional domain-dependent linguistic resources that the system
can use at each stage, and why they are useful. We also present trials showing
that when the domain-dependent llinguistic resources are available, NaturalOWL
produces significantly better texts compared to a simpler verbalizer, and that
the resources can be created with relatively light effort.

NaturalOWL 是一款使用自然语言生成系统的工具，它可以生成多语言的流利和连贯的多句文本描述 OWL 本体中的个体或类，以使信息不仅对计算机程序和领域专家，也对终端用户可见可访问。