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Context-based term identification and extraction for ontology construction

机译:基于上下文的术语识别和本体构建提取

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Ontology construction often requires a domain specific corpus in conceptualizing the domain knowledge; specifically, it is an association of terms, relation between terms and related instances. It is a vital task to identify a list of significant term for constructing a practical ontology. In this paper, we present the use of a context-based term identification and extraction methodology for ontology construction from text document. The methodology is using a taxonomy and Wikipedia to support automatic term identification and extraction from structured documents with an assumption of candidate terms for a topic are often associated with its topic-specific keywords. A hierarchical relationship of super-topics and sub-topics is defined by a taxonomy, meanwhile, Wikipedia is used to provide context and background knowledge for topics that defined in the taxonomy to guide the term identification and extraction. The experimental results have shown the context-based term identification and extraction methodology is viable in defining topic concepts and its sub-concepts for constructing ontology. The experimental results have also proven its viability to be applied in a small corpus / text size environment in supporting ontology construction.
机译:本体构建通常需要特定领域的语料库来概念化领域知识。具体来说,它是术语的关联,术语与相关实例之间的关系。确定重要的术语列表对于构建实用的本体是一项至关重要的任务。在本文中,我们介绍了基于上下文的术语识别和提取方法在文本文档本体构建中的使用。该方法使用分类法和Wikipedia来支持自动术语识别和从结构化文档中提取信息,并假设一个主题的候选术语通常与其特定主题的关键字相关联。分类法定义了超级主题和子主题的层次关系,同时,维基百科用于为分类法中定义的主题提供上下文和背景知识,以指导术语识别和提取。实验结果表明,基于上下文的术语识别和提取方法在定义主题概念及其子概念以构建本体时是可行的。实验结果还证明了其在支持主体构建的小型语料库/文本大小环境中的可行性。

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