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Towards Ontology-Driven RDF Analytics

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

The RDF data model lends itself to the organization of graph-structured data. The analysis of such data requires specific tools and techniques broadly summarized as RDF analytics. In particular, traditional approaches to the aggregation of multidimensional data do not apply directly to RDF data due to the lack of information regarding the granularity level of the data and unclear semantics of aggregation. Ontologies, however, may provide the additional information required for RDF data aggregation. Using a vocabulary for ontology-based RDF analytics in conjunction with existing domain ontologies, modelers may declaratively specify aggregated views over RDF data. In this paper we describe the fundamentals of ontology-driven RDF analytics based on RDF, RDF Schema, and SPARQL. We present a proof-of-concept implementation of the basic approach that uses open-source technology, thereby demonstrating feasibility. We further discuss possible future extensions to the basic approach. Keywords: Business intelligence – Semantic web – SPARQL
Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling, Proceedings ER 2015 Workshops, AHA, CMS, EMoV, MoBiD, MORE-BI, MReBA, QMMQ, and {SCME}
EditorsManfred A. Jeusfeld, Kamalakar Karlapalem
PublisherSpringer Verlag
Pages210-219
Number of pages10
Volume9382
ISBN (Print)978-3-319-25746-4
DOIs
Publication statusPublished - Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9382
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of science

  • 102 Computer Sciences
  • 102010 Database systems
  • 102015 Information systems
  • 102016 IT security
  • 102025 Distributed systems
  • 102027 Web engineering
  • 102028 Knowledge engineering
  • 102030 Semantic technologies
  • 502050 Business informatics
  • 503008 E-learning

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Management and Innovation

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