Personalized Facets for Semantic Search Using Linked Open Data with Social Networks

The main aim of this research is to deal with semantic search based on personalized facets in linked open data. User profile is learned from his/her activities and preferences in social networks using tf-idf feature vector model. A faceted graph visualization for result collaborative filtering is proposed. The facets are vertices representing ontological concepts. Other vertices represent instances belonging to the concepts, which are known as facets values. The vertices are highlighted by matching between facets and user profile in order to individually produce search interfaces. The ties between vertices are ontological relations or properties considering as variables/attributes of facets. An algorithm to construct the faceted graph visualization and collaboratively filter search result is also provided. The faceted search method presented here is implemented to demonstrate these ideas.