A distributed inverted indexing scheme for large-scale RDF data

Abstract

With the development of the Linked Data project, enormous RDF data have been published on the Web. A scalable system is required to provide an efficient retrieval for large-scale RDF data. This paper presents a distributed inverted indexing scheme for large-scale RDF data. A scalable inverted index is built using the underlying data structure of Cassandra which is a distributed key-value storage system. We optimize the indexing scheme with the characteristics of RDF data model to effectively support the fast keyword search. The loading, encoding and indexing procedures are implemented for RDF data simultaneously using the MapReduce framework. The experimental results show that our indexing scheme can effectively support keyword retrieval for large-scale RDF data. © 2012 Springer-Verlag.