Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: An application to Alzheimer's disease
Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: An application to Alzheimer's disease
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DOI：
10.1016/j.jbi.2013.12.013
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发表时间：
2014-04-01
影响因子：
4.5
通讯作者：
Mougin, Fleur
中科院分区：
文献类型：
--
作者：
Drame, Khadim;Diallo, Gayo;Mougin, Fleur
本体论是共享和交换知识的有用工具。然而本体构建非常复杂并且通常耗时。在本文中，我们提出了一种从文本和术语本体资源构建双语领域本体的方法，用于文本文档的语义注释和信息检索。该方法结合了两种方法：从文本中进行本体学习和现有术语资源的重用。它包括四个步骤：（i）使用文本分析工具从特定领域的语料库（法语和英语）中提取术语，（ii）将术语聚类为根据 UMLS Metathesaurus 组织的概念，（iii）通过使用并行语料库对齐法语和英语术语以及整合新概念来丰富本体，（iv）由领域专家对结果进行细化和验证。这些经过验证的结果被形式化为专门用于阿尔茨海默病和相关综合征的领域本体，可在线获取 (http://lesim.isped.u-bordeaux2.fr/SembiP/ressources/ontoAD.owl)。后者目前包括 5765 个概念，由 7499 个分类关系和 10,889 个非分类关系链接。在这些结果中，创建了 UMLS 中缺少的 439 个概念，并添加了 608 个新的同义法语术语。所提出的方法足够灵活，可以应用于其他领域。 (C) 2013 Elsevier Inc. 保留所有权利。
Ontologies are useful tools for sharing and exchanging knowledge. However ontology construction is complex and often time consuming. In this paper, we present a method for building a bilingual domain ontology from textual and termino-ontological resources intended for semantic annotation and information retrieval of textual documents. This method combines two approaches: ontology learning from texts and the reuse of existing terminological resources. It consists of four steps: (i) term extraction from domain specific corpora (in French and English) using textual analysis tools, (ii) clustering of terms into concepts organized according to the UMLS Metathesaurus, (iii) ontology enrichment through the alignment of French and English terms using parallel corpora and the integration of new concepts, (iv) refinement and validation of results by domain experts. These validated results are formalized into a domain ontology dedicated to Alzheimer's disease and related syndromes which is available online (http://lesim.isped.u-bordeaux2.fr/SemBiP/ressources/ontoAD.owl). The latter currently includes 5765 concepts linked by 7499 taxonomic relationships and 10,889 non-taxonomic relationships. Among these results, 439 concepts absent from the UMLS were created and 608 new synonymous French terms were added. The proposed method is sufficiently flexible to be applied to other domains. (C) 2013 Elsevier Inc. All rights reserved.