Social Relation Based Scalable Semantic Search Refinement

One of the major problems for semantic search at Web scale is that the search results on the semantic data might be huge and the users have to browse to find the most relevant ones. Plus, due to the reason for the context, user requirement may diverse even the input query may be the same. In this paper, we try to achieve scalability in semantic search through social relation diversity of different users. Namely, we utilize one of the major context for users, social relations, to help refining the semantic search process. Social network based interest retention model is developed on top of user name based social relations, and is designed to be used in more wider range of Web scale semantic search tasks. The experiments are based on the SwetoDBLP dataset, and we can conclude that proposed method is potentially effective to help users find most relevant search results in a scalable environment.