Utilizing DBpedia via CBR Approach of Recommender System

Abstract

Using Linked Open Data Cloud for knowledge extraction is a challenging as well as budding research field. Knowledge extraction is essential work of developing a Recommendation System (RS). Traditional context analyzers of Content Based Recommendation (CBR) are no more sufficient in current web era of semantics. This problem can be removed through generation of features from the logic of semantics presents implicitly in structured format in the web. This promising area merge various domains and technologies namely, semantic web, machine learning, personalization and information retrieval for achieving a good recommendation.This paper discussed about the research questions and challenges that originate from the extraction of semantic knowledge. This semantics can be accessed from a huge open source data cloud that are linked in meaningful way and named as Linked Open Data Cloud (LOD). The implementation includes key methods for how to gain semantically enriched data related to particular items. Paper explains the book domain residing inside the DBpedia datasets, which consist of cross domain information.