摘要翻译：
在本体之上构建规则是语义网逻辑层的最终目标。为此，目前正在讨论针对这一层的一种特殊标记语言。它旨在继承诸如$\Mathcal{AL}$-log等混合知识表示和推理系统的传统，该系统集成了描述逻辑$\Mathcal{ALC}$和无函数的Horn小句语言\textsc{Datalog}。在本文中，我们考虑了语义Web中这些规则的自动获取问题。我们提出了一个通用的规则归纳框架，该框架采用归纳逻辑程序设计的方法，并依赖于$\mathcal{AL}$-log的表达和演绎能力。无论归纳的范围（描述和预测）是什么，这个框架都是有效的。然而，为了说明的目的，我们还讨论了框架的一个实例化，它旨在描述并证明在本体精化中是有用的。关键词：归纳逻辑程序设计，混合知识表示和推理系统，本体，语义网。注：出现在逻辑程序设计理论与实践(TPLP)中
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英文标题：
《Building Rules on Top of Ontologies for the Semantic Web with Inductive
  Logic Programming》
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作者：
Francesca A. Lisi
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最新提交年份：
2007
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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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as $\mathcal{AL}$-log that integrates the description logic $\mathcal{ALC}$ and the function-free Horn clausal language \textsc{Datalog}. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of $\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement.   Keywords: Inductive Logic Programming, Hybrid Knowledge Representation and Reasoning Systems, Ontologies, Semantic Web.   Note: To appear in Theory and Practice of Logic Programming (TPLP)
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PDF链接：
https://arxiv.org/pdf/0711.1814