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With the large volume of unstructured data that increases constantly on theweb, the motivation of representing the knowledge in this data in themachine-understandable form is increased. Ontology is one of the majorcornerstones of representing the information in a more meaningful way on thesemantic Web. The current ontology repositories are quite limited either fortheir scope or for currentness. In addition, the current ontology extractionsystems 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 tointroduce a proposal of automatically extracting semantic concepts andrelations from scientific publications. This paper suggests new types ofsemantic relations and points out of using deep learning (DL) models forsemantic relation extraction.

Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning