Ontology Learning from Text Using Relational Concept Analysis
Ontology Learning from Text Using Relational Concept Analysis
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DOI：
10.1109/mcetech.2008.29
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发表时间：
2008-01
期刊：
影响因子：
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通讯作者：
M. Hacene;A. Napoli;Petko Valtchev;Y. Toussaint;R. Bendaoud
中科院分区：
文献类型：
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作者：
M. Hacene;A. Napoli;Petko Valtchev;Y. Toussaint;R. Bendaoud
我们提出了一种从文本半自动构建本体的方法，其核心组件是关系概念分析（RCA）框架，该框架将形式概念分析（FCA）（一种用于发现对象x属性表中的抽象的格理论范式）扩展到通过自身属性和个体间链接描述的多种个体的处理。作为一种预处理，文本分析用于将文档集合转换为一组数据表或上下文以及上下文之间的关系。然后 RCA 将它们转化为一组具有相互关联的概念的概念格。通过将相关的格元素转化为本体概念和关系，即分类学或横向概念和关系，以半自动的方式从格中导出核心本体。通过使用 RCA 从最初识别的横向关系中抽象出新的横向关系，本体论得以进一步细化。我们还讨论了将该方法应用于天文学文本的结果。
We propose an approach for semi-automated construction of ontologies from text whose core component is a relational concept analysis (RCA) framework which extends formal concept analysis (FCA), a lattice-theory paradigm for discovering abstractions within objects x attributes tables, to the processing of several sorts of individuals described both by own properties and inter-individual links. As a pre-processing, text analysis is used to transform a document collection into a set of data tables, or contexts, and inter-context relations. RCA then turns these into a set of concept lattices with inter-related concepts. A core ontology is derived from the lattices in a semi-automated manner, by translating relevant lattice elements into ontological concepts and relations, i.e., either taxonomic or transversal ones. The ontology is further refined by abstracting new transversal relations from the initially identified ones using RCA. We discuss as well the results of an application of the method to astronomy texts.