CLC number: TP311
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2012-02-27
Cited: 3
Clicked: 7154
Hang Zhang, Wei Hu, Yu-zhong Qu. VDoc+: a virtual document based approach for matching large ontologies using MapReduce[J]. Journal of Zhejiang University Science C, 2012, 13(4): 257-267.
@article{title="VDoc+: a virtual document based approach for matching large ontologies using MapReduce",
author="Hang Zhang, Wei Hu, Yu-zhong Qu",
journal="Journal of Zhejiang University Science C",
volume="13",
number="4",
pages="257-267",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1101007"
}
%0 Journal Article
%T VDoc+: a virtual document based approach for matching large ontologies using MapReduce
%A Hang Zhang
%A Wei Hu
%A Yu-zhong Qu
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 4
%P 257-267
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1101007
TY - JOUR
T1 - VDoc+: a virtual document based approach for matching large ontologies using MapReduce
A1 - Hang Zhang
A1 - Wei Hu
A1 - Yu-zhong Qu
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 4
SP - 257
EP - 267
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1101007
Abstract: Many ontologies have been published on the semantic Web, to be shared to describe resources. Among them, large ontologies of real-world areas have the scalability problem in presenting semantic technologies such as ontology matching (OM). This either suffers from too long run time or has strong hypotheses on the running environment. To deal with this issue, we propose a three-stage mapReduce-based approach V-Doc+ for matching large ontologies, based on the mapReduce framework and virtual document technique. Specifically, two mapReduce processes are performed in the first stage to extract the textual descriptions of named entities (classes, properties, and instances) and blank nodes, respectively. In the second stage, the extracted descriptions are exchanged with neighbors in Resource Description Framework (RDF) graphs to construct virtual documents. This extraction process also benefits from the mapReduce-based implementation. A word-weight-based partitioning method is proposed in the third stage to conduct parallel similarity calculation using the term frequency–inverse document frequency (TF-IDF) model. Experimental results on two large-scale real datasets and the benchmark testbed from Ontology Alignment Evaluation Initiative (OAEI) are reported, showing that the proposed approach significantly reduces the run time with minor loss in precision and recall.
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