Graph mining meets the Semantic Web

Sangkeun Lee; Sreenivas R. Sukumar; Seung Hwan Lim

doi:10.1109/ICDEW.2015.7129544

Graph mining meets the Semantic Web

Sangkeun Lee, Sreenivas R. Sukumar, Seung Hwan Lim

Discrete Algorithms

Oak Ridge National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English
Title of host publication	ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops
Publisher	IEEE Computer Society
Pages	53-58
Number of pages	6
ISBN (Electronic)	9781479984411
DOIs	https://doi.org/10.1109/ICDEW.2015.7129544
State	Published - Jun 19 2015
Event	2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 - Seoul, Korea, Republic of Duration: Apr 13 2015 → Apr 17 2015

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2015-June
ISSN (Print)	1084-4627

Conference

Conference	2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
Country/Territory	Korea, Republic of
City	Seoul
Period	04/13/15 → 04/17/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Access to Document

10.1109/ICDEW.2015.7129544

Cite this

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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.",

author = "Sangkeun Lee and Sukumar, {Sreenivas R.} and Lim, {Seung Hwan}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 ; Conference date: 13-04-2015 Through 17-04-2015",

year = "2015",

month = jun,

day = "19",

doi = "10.1109/ICDEW.2015.7129544",

language = "English",

series = "Proceedings - International Conference on Data Engineering",

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Lee, S, Sukumar, SR & Lim, SH 2015, Graph mining meets the Semantic Web. in ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops., 7129544, Proceedings - International Conference on Data Engineering, vol. 2015-June, IEEE Computer Society, pp. 53-58, 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015, Seoul, Korea, Republic of, 04/13/15. https://doi.org/10.1109/ICDEW.2015.7129544

Graph mining meets the Semantic Web. / Lee, Sangkeun; Sukumar, Sreenivas R.; Lim, Seung Hwan.
ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops. IEEE Computer Society, 2015. p. 53-58 7129544 (Proceedings - International Conference on Data Engineering; Vol. 2015-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Graph mining meets the Semantic Web

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