COLINA: a method for ranking SPARQL query results through content and link analysis
Azam Feyznia, Mohsen Kahani, Fattane Zarrinkalam · 2014
Abstract. The growing amount of Linked Data increases the importance of se-mantic search engines for retrieving information. Users often examine the first few results among all returned results. Therefore, using an appropriate ranking algorithm has a great effect on user satisfaction. To the best of our knowledge, all previous methods for ranking SPARQL query results are based on popularity calculation and currently there isn’t any method for calculating the relevance of results with SPARQL query. However, the proposed ranking method of this pa-per calculates both relevancy and popularity ranks for SPARQL query results through content and link analysis respectively. It calculates the popularity rank by generalizing PageRank method on a graph with two layers, data sources and semantic documents. It also assigns weights automatically to different semantic links. Further, the relevancy rank is based on the relevance of semantic docu-ments with SPARQL query.