# Context and Keyword Extraction in Plain Text Using a Graph Representation

## TL;DR

This paper introduces a novel indexing support system that uses ontology and graph representations to extract contextualized keywords from plain text documents, aiding archivists in document indexing.

## Contribution

It presents an innovative method leveraging ontologies and graph structures to improve keyword extraction and document indexing support.

## Key findings

- Effective extraction of contextualized keywords demonstrated
- Utilized Wikipedia's category links as a resource
- Enhanced indexing support for specialized documents

## Abstract

Document indexation is an essential task achieved by archivists or automatic indexing tools. To retrieve relevant documents to a query, keywords describing this document have to be carefully chosen. Archivists have to find out the right topic of a document before starting to extract the keywords. For an archivist indexing specialized documents, experience plays an important role. But indexing documents on different topics is much harder. This article proposes an innovative method for an indexing support system. This system takes as input an ontology and a plain text document and provides as output contextualized keywords of the document. The method has been evaluated by exploiting Wikipedia's category links as a termino-ontological resources.