摘要翻译：
情感刺激的成功管理是情感计算及其相关研究的一个关键问题。作为人工智能的一个子领域，交流不仅涉及到能够识别、解释和处理人类情感的计算机系统及其配套硬件的设计，而且涉及到能够以有序和受控的方式触发人类情感反应的系统的开发。这要求在从带有情感注释的数据库中提取数据时达到最大的精确度和效率虽然这些数据库确实使用关键字或标记来描述语义内容，但它们没有提供必要的灵活性或有效提取相关情感内容所需的杠杆作用。因此，在这种程度上，我们提出引入本体作为描述情感注释数据的新范式。基于语义属性选择和排序数据的能力对于任何涉及元数据、语义和本体排序的研究都是至关重要的，如语义Web或社会语义桌面，本文描述的方法也促进了这些领域的重用。
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英文标题：
《Tagging multimedia stimuli with ontologies》
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作者：
Marko Horvat, Sinisa Popovic, Nikola Bogunovic and Kresimir Cosic
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最新提交年份：
2009
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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：
  Successful management of emotional stimuli is a pivotal issue concerning Affective Computing (AC) and the related research. As a subfield of Artificial Intelligence, AC is concerned not only with the design of computer systems and the accompanying hardware that can recognize, interpret, and process human emotions, but also with the development of systems that can trigger human emotional response in an ordered and controlled manner. This requires the maximum attainable precision and efficiency in the extraction of data from emotionally annotated databases While these databases do use keywords or tags for description of the semantic content, they do not provide either the necessary flexibility or leverage needed to efficiently extract the pertinent emotional content. Therefore, to this extent we propose an introduction of ontologies as a new paradigm for description of emotionally annotated data. The ability to select and sequence data based on their semantic attributes is vital for any study involving metadata, semantics and ontological sorting like the Semantic Web or the Social Semantic Desktop, and the approach described in the paper facilitates reuse in these areas as well.
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PDF链接：
https://arxiv.org/pdf/0903.0829