SPRING: ranking the results of SPARQL queries on linked data

Abstract

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.