摘要翻译：
本文介绍了OWL驱动的岩石学术语知识和事实的形式化表示和推理系统的开发工作。我们项目的长期目标是为各种知识的大规模集成提供坚实的基础，包括基本术语、岩石分类算法、发现和报告。我们在此描述我们为实现这一目标所采取的三个步骤。首先，我们开发了一个将火成岩样本数据库转换为受控自然语言(CNL)文本的半自动过程，然后将OWL本体集合转换为文本。其次，我们创建了一个OWL本体，其中包含了目前在自然语言叙词表中描述的重要岩石学术语。我们描述了一个从领域专家那里收集定义的工具原型。第三，我们提出了一种对岩石样品分类的工业标准进行形式化的方法，该方法需要OWL2中的线性方程组。最后，我们讨论了在岩石学中使用语义技术所带来的一系列机会，并概述了这一领域的未来工作。
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英文标题：
《Towards OWL-based Knowledge Representation in Petrology》
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作者：
Alex Shkotin, Vladimir Ryakhovsky, Dmitry Kudryavtsev
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents our work on development of OWL-driven systems for formal representation and reasoning about terminological knowledge and facts in petrology. The long-term aim of our project is to provide solid foundations for a large-scale integration of various kinds of knowledge, including basic terms, rock classification algorithms, findings and reports. We describe three steps we have taken towards that goal here. First, we develop a semi-automated procedure for transforming a database of igneous rock samples to texts in a controlled natural language (CNL), and then a collection of OWL ontologies. Second, we create an OWL ontology of important petrology terms currently described in natural language thesauri. We describe a prototype of a tool for collecting definitions from domain experts. Third, we present an approach to formalization of current industrial standards for classification of rock samples, which requires linear equations in OWL 2. In conclusion, we discuss a range of opportunities arising from the use of semantic technologies in petrology and outline the future work in this area.
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PDF链接：
https://arxiv.org/pdf/1106.1510