Graph mining meets the Semantic Web

• Published in: 2015 31st IEEE International Conference on Data Engineering Workshops pp. 53 - 58
• Main Authors: Lee, Sangkeun, Sukumar, Sreenivas R., Seung-Hwan Lim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2015
• Subjects: Algorithm design and analysis; Communities; Data mining; Database languages; Resource description framework; Software algorithms
• DOI: 10.1109/ICDEW.2015.7129544

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.