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

作　　者：Hang ZHANG Wei HU Yu-zhong QU 

机构地区：[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

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2012年第4期257-267,共11页浙江大学学报C辑（计算机与电子（英文版）

基　　金：supported by the National Natural Science Foundation of China (No.61003018);the Natural Science Foundation of Jiangsu Province,China (No.BK2011189);the National Social Science Foundation of China (No.11AZD121)

摘　　要：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.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：Ontology matching Virtual document MAPREDUCE TF-IDF Semantic Web 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]