An effective method of large scale ontology matching.
An effective method of large scale ontology matching.
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DOI：
10.1186/2041-1480-5-44
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发表时间：
2014
影响因子：
1.9
通讯作者：
Diallo G
中科院分区：
文献类型：
--
作者：
Diallo G
我们目前面临着通过各种基于知识的应用程序访问的异质生物医学数据源的激增。这些数据通过越来越广泛和广泛传播的知识组织系统进行注释，范围从简单的术语和结构化词汇到形式本体。为了解决由于这些本体的异构性而产生的互操作性问题，通常执行对齐任务。然而，尽管已经做出了大量努力来提供自动对齐包含数百或数千个实体的小本体的工具，但很少有人关注生命科学领域中的大尺寸本体的匹配。设计并实现了一种有效的大规模本体匹配方法ServOMap。它是一个快速、高效、高精度的系统，能够对包含数十万个实体的输入本体进行匹配。该系统被包括在2012年和2013年版的本体对齐评估倡议活动中，表现非常好。它被评为大型本体匹配的顶级系统之一。提出了一种基于信息检索技术的大规模本体匹配方法，并结合词法和机器学习的上下文相似度计算来生成候选映射。它特别适用于生命科学领域，因为该领域的许多本体受益于来自统一医学语言系统的同义词，可以被我们的IR策略使用。我们实现的ServOMap系统能够以高效的计算时间处理数十万个实体。
We are currently facing a proliferation of heterogeneous biomedical data sources accessible through various knowledge-based applications. These data are annotated by increasingly extensive and widely disseminated knowledge organisation systems ranging from simple terminologies and structured vocabularies to formal ontologies. In order to solve the interoperability issue, which arises due to the heterogeneity of these ontologies, an alignment task is usually performed. However, while significant effort has been made to provide tools that automatically align small ontologies containing hundreds or thousands of entities, little attention has been paid to the matching of large sized ontologies in the life sciences domain. We have designed and implemented ServOMap, an effective method for large scale ontology matching. It is a fast and efficient high precision system able to perform matching of input ontologies containing hundreds of thousands of entities. The system, which was included in the 2012 and 2013 editions of the Ontology Alignment Evaluation Initiative campaign, performed very well. It was ranked among the top systems for the large ontologies matching. We proposed an approach for large scale ontology matching relying on Information Retrieval (IR) techniques and the combination of lexical and machine learning contextual similarity computing for the generation of candidate mappings. It is particularly adapted to the life sciences domain as many of the ontologies in this domain benefit from synonym terms taken from the Unified Medical Language System and that can be used by our IR strategy. The ServOMap system we implemented is able to deal with hundreds of thousands entities with an efficient computation time.
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影响因子：
4.5
作者：
Drame, Khadim;Diallo, Gayo;Mougin, Fleur
通讯作者：
Mougin, Fleur
影响因子：
1.9
作者：
He, Yongqun;Sarntivijai, Sirarat;Smith, Barry
通讯作者：
Smith, Barry
影响因子：
14.9
作者：
Bodenreider, O
通讯作者：
Bodenreider, O
影响因子：
4.8
作者：
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通讯作者：
Diallo, G.