Document Concept Hierarchy Generation by Extracting Semantic Tree Using Knowledge Graph

Abstract

Semantic Web, as an extension of traditional web, is concerned about the vast amount of unstructured data, and with its motive to make the entire knowledge content machine readable, as well as machine interpretable, all the processes of structuring the data is highly significant. Knowledge representation in trees has been a familiar mechanism for some time. However, such representations lack in existence when it comes to document content. In this paper, we present a general mechanism that can generate a representation of the concepts of any document in the form of knowledge trees. We further gather knowledge from knowledge graphs and analyze these data by mapping it with an existing ontology. Finally, we explain how this can be used to create hierarchical concept recommendations to make the documents search efficient.