Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Constructing virtual documents for ontology matching
Proceedings of the 15th international conference on World Wide Web
Ontology Matching
Matching large schemas: Approaches and evaluation
Information Systems
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
ACM Computing Surveys (CSUR)
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
Semantic Web Multimedia Metadata Retrieval: A Music Approach
PCI '09 Proceedings of the 2009 13th Panhellenic Conference on Informatics
Rewrite techniques for performance optimization of schema matching processes
Proceedings of the 13th International Conference on Extending Database Technology
An adaptive ontology mapping approach with neural network based constraint satisfaction
Web Semantics: Science, Services and Agents on the World Wide Web
Efficient parallel set-similarity joins using MapReduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On matching large life science ontologies in parallel
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Matching large ontologies based on reduction anchors
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Ontology matching is a crucial task for data integration and management on the Semantic Web. The ontology matching techniques today can solve many problems from heterogeneity of ontologies to some extent. However, for matching large ontologies, most ontology matchers take too long run time and have strong requirements on running environment. Based on the MapReduce framework and the virtual document technique, in this paper, we propose a 3-stage MapReduce-based approach called V-Doc+ for matching large ontologies, which significantly reduces the run time while keeping good precision and recall. Firstly, we establish four MapReduce processes to construct virtual document for each entity (class, property or instance), which consist of a simple process for the descriptions of entities, an iterative process for the descriptions of blank nodes and two processes for exchanging the descriptions with neighbors. Then, we use a word-weight-based partition method to calculate similarities between entities in the corresponding reducers. We report our results from two experiments on an OAEI dataset and a dataset from the biology domain. Its performance is assessed by comparing with existing ontology matchers. Additionally, we show how run time is reduced with increasing the size of cluster.