论文标题
通过使用深度学习从科学出版物中提取语义概念和关系
Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning
论文作者
论文摘要
随着大量的非结构化数据在网络上不断增加，以机器无法理解的形式代表该数据中知识的动机增加了。本体论是在语义网络上以更有意义的方式代表信息的主要基石之一。当前的本体论存储库的范围或当前性都非常有限。此外，当前的本体提取系统具有许多缺点和缺点，例如使用一个小数据集，具体取决于大量预定义的模式来提取语义关系，并提取了几种类型的关系。本文的目的是介绍一项自动从科学出版物中提取语义概念和关系的建议。本文提出了新型的语义关系类型，并指出了使用深度学习（DL）模型进行语义关系提取。
With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper suggests new types of semantic relations and points out of using deep learning (DL) models for semantic relation extraction.
