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.
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)
