Abstract. With billions of triples in the Linked Open Data cloud, which continues to grow exponentially, very challenging tasks begin to emerge related to the exploitation of large-scale reasoning. A considerable amount of work has been done in the area of using Information Retrieval methods to address these problems. However, although applied models work on Web scale, they downgrade the semantics contained in an RDF graph by observing each physical resource as a ’bag of words (URIs/literals)’. Distributional statistic methods can address this problem by capturing the structure of the graph more efficiently. However, these methods are continually confronting with efficiency and scalability problems on serial computing architectures due to their computational complexity. In this paper, we describe a parallelization algorithm of one such method (Random Indexing) based on the Message-Passing Interface (MPI), that enables efficient utilization of high performance parallel computers. Our evaluation results show significant performance improvement.

We propose an approach for searching large RDF graphs, using advanced vector space models, and in particular, Random Indexing (RI). We first generate documents from an RDF Graph, and then index them using RI in order to generate a semantic index, which is then used to find similarities between graph nodes. We have experimented with large RDF graphs in the domain of life sciences and engaged the domain experts in two stages: firstly, to generate a set of keywords of interest to them, and secondly to judge on the quality of the output of the Random Indexing method, which generated a set of similar terms (literals and