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Online Reasoning for Ontology-Based Error Detection in Text

机译:文本中基于本体的错误检测的在线推理

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摘要

Detecting error in text is a difficult task. Current methods use a domain ontology to identify elements in the text that contradicts domain knowledge. Yet, these methods require manually defining the type of errors that are expected to be found in the text before applying them. In this paper we propose a new approach that uses logic reasoning to detect errors in a statement from text online. Such approach applies Information Extraction to transform text into a set of logic clauses. The logic clauses are incorporated into the domain ontology to determine if it contradicts the ontology or not. If the statement contradicts the domain ontology, then the statement is incorrect with respect to the domain knowledge. We have evaluated our proposed method by applying it to a set of written summaries from the domain of Ecosystems. We have found that this approach, although depending on the quality of the Information Extraction output, can identify a significant amount of errors. We have also found that modeling elements of the ontology (i.e., property domain and range) likewise affect the capability of detecting errors.
机译:检测文本中的错误是一项艰巨的任务。当前的方法使用领域本体来识别文本中与领域知识相矛盾的元素。但是,这些方法需要在应用它们之前手动定义预期在文本中发现的错误的类型。在本文中,我们提出了一种新方法,该方法使用逻辑推理从在线文本中检测语句中的错误。这种方法应用信息提取将文本转换为一组逻辑子句。逻辑子句被合并到领域本体中,以确定它是否与本体矛盾。如果该陈述与领域本体矛盾,则该陈述在领域知识方面是不正确的。我们通过将其应用于生态系统领域的一组书面摘要中,对我们提出的方法进行了评估。我们发现,尽管这种方法取决于信息提取输出的质量,但可以识别大量错误。我们还发现,本体的建模元素(即,属性域和范围)同样影响检测错误的能力。

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