VDoc+: a virtual document based approach for matching large ontologies using MapReduce

Hang ZHANG; Wei HU; Yu-zhong QU

doi:10.1631/jzus.C1101007

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VDoc+: a virtual document based approach for matching large ontologies using MapReduce

Special Issue on Semantic Technologies for a Smarter Web | Updated：2022-12-09

- VDoc+: a virtual document based approach for matching large ontologies using MapReduce
- VDoc+: a virtual document based approach for matching large ontologies using MapReduce
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 4, Pages: 257-267(2012)
- Affiliations：
  
  1.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  2.Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China
- Author bio：
  
  [ "Hang ZHANG, E-mail: hangzhang@smail.nju.edu.cn" ]
  Wei HU, E-mail: whu@nju.edu.cn
- Funds：
- DOI：10.1631/jzus.C1101007
  CLC： TP311
- Received：05 August 2011，
  
  Revised：2012-;2-27，
  
  Published：2012-04
- Accepted：
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Hang ZHANG, Wei HU, Yu-zhong QU. VDoc+: a virtual document based approach for matching large ontologies using MapReduce[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(4): 257-267.
DOI：

Hang ZHANG, Wei HU, Yu-zhong QU. VDoc+: a virtual document based approach for matching large ontologies using MapReduce[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(4): 257-267. DOI： 10.1631/jzus.C1101007.

摘要

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.

关键词

Keywords

references

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