As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.

随着智能计算系统开发的下一步是增加人类的专业知识和知识，构建强大的可计算性和有据可查的知识库是当务之急。本体通过提供知识表示形式主义来部分地应对这一挑战。然而，剩下的一项主要任务是将这些本体进行具体的应用。基于模型驱动工程原理，本文介绍了用于提取异构数据的通用元模型。设计元模型的目的有两个，即（1）关于所收集知识的来源的通用性需求；（2）坚持与本体论结构接近的结构的意图。还有