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Mining Scholarly Publications for Scientific Knowledge Graph Construction

机译:挖掘科学知识图谱的学术出版物

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摘要

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (ⅰ) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ⅱ) describe an approach for integrating entities and relationships generated by these tools, and (ⅲ) analyse an automatically generated Knowledge Graph including 10,425 entities and 25, 655 relationships in the field of Semantic Web.
机译:在本文中,我们提供了一种初步方法,该方法使用一组NLP和深度学习方法从研究出版物中提取实体和关系,然后将它们集成到知识图谱中。更具体地说,我们(ⅰ)通过使用几种最先进的自然语言处理和文本挖掘工具来应对知识提取的挑战,(ⅱ)描述了一种集成由这些工具生成的实体和关系的方法,并且(ⅲ )分析语义网领域中自动生成的知识图,其中包括10,425个实体以及25、655个关系。

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