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摘要翻译：
在风险管理和危险源识别等关键任务应用中，领域本体的需求越来越迫切。学术界对本体学习的大多数研究对于现实世界的应用仍然是不现实的。其中一个主要问题是依赖于非增量的、稀有的知识和文本资源，以及手工创建的模式和规则。本文报告了在本体构建过程中为解决这些不希望的依赖而进行的工作。使用该系统的一个工作原型进行的初步实验显示，在使用真实世界的文本自动构建高质量领域本体方面具有很好的潜力。
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英文标题：
《Automatic Construction of Lightweight Domain Ontologies for Chemical
Engineering Risk Management》
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作者：
Wilson Wong, Wei Liu, Saujoe Liaw, Nicoletta Balliu, Hongwei Wu, Moses
Tade
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最新提交年份：
2008
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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：
The need for domain ontologies in mission critical applications such as risk management and hazard identification is becoming more and more pressing. Most research on ontology learning conducted in the academia remains unrealistic for real-world applications. One of the main problems is the dependence on non-incremental, rare knowledge and textual resources, and manually-crafted patterns and rules. This paper reports work in progress aiming to address such undesirable dependencies during ontology construction. Initial experiments using a working prototype of the system revealed promising potentials in automatically constructing high-quality domain ontologies using real-world texts.
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PDF链接：
https://arxiv.org/pdf/0812.3478
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