Understanding the structure of a scientific domain and extracting specific information from it is laborious. The high amount of manual effort required to this end indicates that the way knowledge has been structured and visualized until the present day should be improved in software tools. Nowadays, scientific domains are organized based on citation networks or bag-of-words techniques, disregarding the intrinsic semantics of concepts presented in literature documents. We propose a novel approach to structure scientific fields, which uses semantic analysis from natural language texts to construct knowledge graphs. Then, our approach clusters knowledge graphs in their main topics and automatically extracts information such as the most relevant concepts in topics and overlapping concepts between topics. We evaluate the proposed model in two datasets from distinct areas. The results achieve up to 84% of accuracy in the task of document classification without using annotated data to segment topics from a set of input documents. Our solution identifies coherent keyphrases and key concepts considering the dataset used. The SciKGraph framework contributes by structuring knowledge that might aid researchers in the study of their areas, reducing the effort and amount of time devoted to groundwork.

了解科学领域的结构并从中提取特定信息非常费力。为此，需要大量的人工工作，这表明直到今天，知识的结构和可视化方式都应该在软件工具中得到改进。如今，科学领域是基于引文网络或词袋技术来组织的，而无视文献文档中提出的概念的内在语义。我们提出了一种结构科学领域的新方法，该方法使用自然语言文本的语义分析来构建知识图。然后，我们的方法将知识图聚集在其主要主题中，并自动提取信息，例如主题中最相关的概念以及主题之间的重叠概念。我们在来自不同地区的两个数据集中评估了提出的模型。在不使用注释数据对输入文档集中的主题进行细分的情况下，结果可实现文档分类任务中高达84％的准确性。我们的解决方案考虑了所使用的数据集，确定了一致的关键词和关键概念。SciKGraph框架通过构造可帮助研究人员研究其领域的知识来做出贡献，从而减少了投入基础工作的精力和时间。