  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.
