Scalable OWL 2 reasoning for Linked Data

Aidan Hogan*, Jeff Z. Pan, Axel Polleres, Yuan Ren

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

15 Citations (Scopus)


The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to introduce scalable reasoning and querying techniques to Semantic Web researchers as powerful tools to make use of Linked Data and large-scale ontologies, and second, to present interesting research problems for the Semantic Web that arise in dealing with TBox and ABox reasoning in OWL 2. The lecture consists of three parts. The first part will begin with an introduction and motivation for reasoning over Linked Data, including a survey of the use of RDFS and OWL on the Web. The second part will present a scalable, distributed reasoning service for instance data, applying a custom subset of OWL 2 RL/RDF rules (based on a tractable fragment of OWL 2). The third part will present recent work on faithful approximate reasoning for OWL 2 DL. The lecture will include our implementation of the mentioned techniques as well as their evaluations. These notes provide complimentary reference material for the lecture, and follow the three-part structure and content of the lecture.

Original languageEnglish
Title of host publicationReasoning Web
Subtitle of host publicationSemantic Technologies for the Web of Data - 7th International Summer School 2011, Tutorial Lectures
Number of pages76
Publication statusPublished - 9 Sept 2011
Event7th International Summer School 2011 on Reasoning Web: Semantic Technologies for the Web of Data - Galway, Ireland
Duration: 23 Aug 201127 Aug 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6848 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Summer School 2011 on Reasoning Web: Semantic Technologies for the Web of Data


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