We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.

本文介绍了一种使用两层转换阶段的开放式信息提取方法，其中包括从复杂的语言结构中提取命题的核心关系元组和上下文信息，以及使用修辞关系将其语义相关。我们的参考实现 Graphene 在正确构建 n - 元谓词 - 参数结构方面优于现有的开放式 IE 系统，并且我们的框架的转换过程对现有的开放式 IE 方法会产生益处。