skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Graph Mining Meets the Semantic Web

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.

Authors:
 [1];  [2];  [2]
  1. (Matt) [ORNL
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1190754
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Data Engineering meets the Semantic Web (DesWeb) Workshop in conjunction with ICDE 2015, Seoul, South Korea, 20150413, 20150413
Country of Publication:
United States
Language:
English

Citation Formats

Lee, Sangkeun, Sukumar, Sreenivas R, and Lim, Seung-Hwan. Graph Mining Meets the Semantic Web. United States: N. p., 2015. Web.
Lee, Sangkeun, Sukumar, Sreenivas R, & Lim, Seung-Hwan. Graph Mining Meets the Semantic Web. United States.
Lee, Sangkeun, Sukumar, Sreenivas R, and Lim, Seung-Hwan. Thu . "Graph Mining Meets the Semantic Web". United States. doi:.
@article{osti_1190754,
title = {Graph Mining Meets the Semantic Web},
author = {Lee, Sangkeun and Sukumar, Sreenivas R and Lim, Seung-Hwan},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: