Introducing the viewpoint in the resource description using machine learning

TL;DR

This paper presents a machine learning approach to enhance RDF resource descriptions by incorporating viewpoints, improving the relevance of search engine responses based on user interests.

Contribution

It introduces a novel method for converting traditional RDF descriptions into viewpoint-aware descriptions using machine learning on an instanced ontology.

Findings

Viewpoint-aware descriptions improve search relevance

Machine learning effectively detects viewpoints in documents

Enhanced descriptions lead to more relevant user responses

Abstract

Search engines allow providing the user with data information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user's requests.