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Goal-Driven Semi-Automated Generation of Semantic Models

机译:目标驱动的半自动生成语义模型

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The approach taken with OGEP is to parse relevant domain data in the form of unstructured content (or corpus) and use that knowledge to generate and/or evolve an existing ontology. OGEP creates a constant conversation between the corpus parser and a reasoning mechanism (corpus reasoner) that continually formulates potential ontology modifications in the form of hypotheses. These hypotheses are weighted towards contextual relevancy and further reasoned over to provide a confidence measure for use in deciding new assertions to the ontology. The new assertions generated from the corpus reasoner can either be automatically asserted based on confidence measure, or can be asserted by OGEP interacting with a user for final approval. This paper describes the OGEP technology in the context of the architectural components and identifies a potential technology transition path to Scott AFB's Tanker Airlift Control Center (TACC), which serves as the Air Operations Center (AOC) for the Air Mobility Command (AMC).
机译:使用OGEP采取的方法是以非结构化内容(或语料库)的形式解析相关域数据,并使用该知识生成和/或演变现有本体。 OGEP在语料库解析器和推理机制(语料库推理)之间创建了不断的对话,以满足假设形式的潜在本体修改。这些假设对上下文相关性进行加权,并进一步推理以提供用于决定本体论的新断言的置信度量。从语料库推理生成的新断言可以根据置信度量自动断言,或者可以通过OGEP与用户交互以进行最终批准来置信。本文介绍了在架构组件的上下文中的OGEP技术,并识别斯科特AFB的油轮空运控制中心(TACC)的潜在技术转换路径,其用作空移指令(AMC)的空气运营中心(AOC)。

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