摘要翻译：
目前，本体在数据融合中的应用越来越受到人们的关注。事实上，本体被看作是对传感器数据、对象、关系和一般领域理论进行描述和推理的语义工具。此外，不确定性也许是数据融合处理的数据和信息最重要的特征之一。然而，本体论的根本性质意味着本体论只描述断言的、准确的世界事实。已经存在不同的概率、模糊和证据方法来填补这一空白；本文概述了最流行的工具。然而，没有一个工具完全符合我们的目的。因此，我们构造了一个Dempster-Shafer本体，它可以导入到任何特定的领域本体中，并使我们能够以不确定的方式实例化它。我们还开发了一个Java应用程序，支持对这些不确定的本体实例进行推理。
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英文标题：
《Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion
  Applications》
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作者：
Amandine Bellenger (LITIS), Sylvain Gatepaille
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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：
  Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.
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PDF链接：
https://arxiv.org/pdf/1106.3876