Abstract
The Resource Description Framework (RDF) and
SPARQL Protocol and RDF Query Language (SPARQL) were
introduced about a decade ago to enable flexible schema-free data
interchange on the Semantic Web. Today, data scientists use the
framework as a scalable graph representation for integrating,
querying, exploring and analyzing data sets hosted at different
sources. With increasing adoption, the need for graph mining
capabilities for the Semantic Web has emerged. We address
that need through implementation of three popular iterative
Graph Mining algorithms (Triangle count, Connected component
analysis, and PageRank). We implement these algorithms as
SPARQL queries, wrapped within Python scripts. We evaluate
the performance of our implementation on 6 real world data sets
and show graph mining algorithms (that have a linear-algebra
formulation) can indeed be unleashed on data represented as
RDF graphs using the SPARQL query interface.
