Introducing the viewpoint in the resource description using machine learning
This addresses the need for more personalized search results by incorporating viewpoints into resource descriptions, though it appears incremental as it builds on existing RDF and ontology frameworks.
The paper tackles the problem of resource descriptions in search engines not accounting for viewpoints, proposing a machine learning approach to convert classic RDF descriptions into viewpoint-aware ones, with experimental results showing very relevant responses to user requests.
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.