Knowledge based recommendation systems use knowledge about users and products to make recommendations. Knowledge-based recommendations are not dependent on the rating, nor do they have to gather information about a particular user to give recommendations. Knowledge acquisition is the most important task for constructing knowledge-based recommendation system. Acquired knowledge must be represented in some structured machine-readable form, e.g., as ontology to support reasoning about what products meets the user’s requirements. In Semantic Web, knowledge is represented in the form of ontology. Representation of knowledge in structured form of ontology in Semantic Web makes the application of knowledge based recommendations system on Semantic Web very easy, as there is no need to construct knowledge base from scratch. Performance of knowledge based recommendations systems can be enhanced by exploiting ontology reasoning characteristics. This paper explores different techniques used to generate knowledge-based recommendations highlighting the advantages of knowledge based recommendation system over other recommendation techniques.

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Knowledge based Recommendation System in Semantic Web - A Survey

Linking Open Data Project played a vital role in the realization of structured data at World Wide Web stage by methodically demonstrating the importance of machine understandable data for information linking. It has succeeded in bringing up heap of Linked Open Data ranging from geographic to cross-domain datasets which provide huge opportunities for knowledge discovery and mashup application development. Scientific publication datasets are one of main sources in steering today's research work and has a big share in Linked Data Cloud repository. Besides to it, crossdomain linked data datasets e.g. DBpedia, FreeBase etc. has a huge crowd-sourced open knowledge which proved as good resource for content enrichment and interlinking. Noticing the offered added values of scientific publications and cross-domain datasets it will be great to know; what these datasets has to offer each other in Linked Data settings. We are of a view; if these datasets are interlinked can offer adequate information for enrichment of publication related resources i.e. authors and publications. In addition, this will also help to aggregate information of author in a profile which is currently scattered over different linked data resources. However, currently finding and interlinking with appropriate data is still a challenge in Linked Data Cloud. In this paper we presented a case study by interlinking author from scientific publication dataset (DBLP) with person’s record of crossdomain dataset (DBpedia). Moreover, we have investigated to find how much author information is there in DBpedia for indexed DBLP scientific authors and has validated our assumption that meaningful data is present between these datasets.

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Exploring Scientific Publication and Cross Domain Linked Dataset for Similarity - A Case Study

In order to solve the inability of personalized recommendation system on the semantic information processing,the semantic recommendation system model was built,which can descript the semantic sence in user's interest information.A kind of recommendation algorithm was also proposed to calculate the relationship between users and resource.Description logic was used to apply the model in a special domain,two rules was introduced to achieve the ability of interest transmission between different level of semantic concepts both in user profiles and resource profiles.The experiment shows that semantic recommendation system model could produce more related results for particular user than classic methods,it can discovery more new interest which is implicated in the interest concepts.

MU Xiang-wei

Papers

Personalized Recommendation System Modeling in Semantic Web