Matching large ontologies: A divide-and-conquer approach
Matching large ontologies: A divide-and-conquer approach
复制标题
匹配大型本体：分而治之的方法
DOI：
10.1016/j.datak.2008.06.003
复制
发表时间：
2008-10-01
影响因子：
2.5
通讯作者：
Cheng, Gong
中科院分区：
文献类型：
--
作者：
Hu, Wei;Qu, Yuzhong;Cheng, Gong
随着语义网的发展，本体不断涌现。本体匹配是在使用不同但相关的本体的(语义)Web应用程序之间建立互操作性的重要方法。由于其大小和单一性，涉及现实世界领域的大型本体给本体匹配技术带来了新的挑战。在本文中，我们提出了一种分而治之的方法来匹配大型本体。我们开发了一种基于结构的划分算法，将每个本体的实体划分为一组小的簇，并通过为这些簇分配RDF语句来构造块。然后，基于预先计算的锚点匹配来自不同本体的块，并选择相似度较高的块映射。最后，使用两个功能强大的匹配器V-Doc和GMO来发现块映射中的比对。在人工数据集和真实数据集上的综合评估表明，该方法既解决了可扩展性问题，又获得了良好的查准率和召回率，并显著减少了执行时间。(C)2008爱思唯尔B.V.保留所有权利。
Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies. We develop a structure-based partitioning algorithm, which partitions entities of each ontology into a set of small clusters and constructs blocks by assigning RDF Sentences to those clusters. Then, the blocks from different ontologies are matched based on precalculated anchors, and the block mappings holding high similarities are selected. Finally, two powerful matchers, V-Doc and GMO, are employed to discover alignments in the block mappings. Comprehensive evaluation on both synthetic and real world data sets demonstrates that our approach both solves the scalability problem and achieves good precision and recall with significant reduction of execution time. (C) 2008 Elsevier B.V. All rights reserved.