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Large Scale Fuzzy pD ∗ Reasoning Using (2011)
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| Venue: | MapReduce’, in International Semantic Web Conference |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Liu11largescale,
author = {Chang Liu and Guilin Qi and Haofen Wang and Yong Yu},
title = {Large Scale Fuzzy pD ∗ Reasoning Using},
booktitle = {MapReduce’, in International Semantic Web Conference},
year = {2011},
pages = {405--420}
}
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Abstract
Abstract. The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD ∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic data under fuzzy pD ∗ semantics (i.e., an extension of OWL pD ∗ semantics with fuzzy vagueness). To the best of our knowledge, this is the first work to investigate how MapReduce can help to solve the scalability issue of fuzzy OWL reasoning. While most of the optimizations used by the existing MapReduce framework for pD ∗ semantics are also applicable for fuzzy pD ∗ semantics, unique challenges arise when we handle the fuzzy information. We identify these key challenges, and propose a solution for tackling each of them. Furthermore, we implement a prototype system for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD ∗ semantics. 1
Keyphrases
large scale fuzzy pd pd semantics fuzzy pd semantics mapreduce framework fuzzy vagueness scalability issue semantic data scalable reasoning unique challenge earlier work first work fuzzy owl reasoning running time evaluation purpose prototype system key challenge owl pd semantics large scale fuzzy information data-intensive task state-of-the-art inference engine experimental result promising result
