The BioLexicon: a large-scale terminological resource for biomedical text mining.
The BioLexicon: a large-scale terminological resource for biomedical text mining.
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DOI：
10.1186/1471-2105-12-397
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发表时间：
2011-10-12
影响因子：
3
通讯作者：
Ananiadou S
中科院分区：
文献类型：
--
作者：
Thompson P;McNaught J;Montemagni S;Calzolari N;del Gratta R;Lee V;Marchi S;Monachini M;Pezik P;Quochi V;Rupp CJ;Sasaki Y;Venturi G;Rebholz-Schuhmann D;Ananiadou S
由于生物医学文献体的迅速扩大，生物学家需要日益复杂和高效的系统来帮助他们搜索相关信息。此类系统应考虑用于表示生物医学概念的多种书面变体，并允许用户搜索涉及这些概念的特定知识（或事件），例如蛋白质-蛋白质相互作用。此类功能需要访问有关生物医学文献中使用的单词的详细信息。现有的数据库和本体通常有特定的重点并且面向人类使用。因此，生物学知识分散在许多资源中，这些资源通常不会试图解释文献中出现的大量且频繁变化的变异集。此外，此类资源通常不提供有关术语在描述事件的文本中如何相互关联的信息。本文概述了生物医学领域的大规模词汇和概念资源 BioLexicon 的设计、构建和评估。文本挖掘工具可以在多个层面上利用该资源，例如词性标记、生物医学实体识别以及提取它们所涉及的事件。因此，生物词典必须考虑生物医学文本中单词的真实用法。特别是，BioLexicon 将多个现有数据资源中的不同类型术语收集到一个统一的存储库中，并使用从生物医学文献中自动提取的新术语变体来增强它们。通过包含生物学相关的动词（通常围绕其组织事件）以及从特定领域的文本获取的有关语法和语义行为的典型模式的信息，可以促进事件的提取。为了促进互操作性，BioLexicon 使用词汇标记框架（ISO 标准）进行建模。 BioLexicon 包含超过 220 万个词汇条目和超过 180 万个术语变体，以及超过 330 万个语义关系，其中包括超过 200 万个同义词关系。它的利用可以使应用程序开发人员和用户受益。我们通过描述将资源集成到许多不同的工具中并评估这可以带来的性能改进来展示一些这样的好处。
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.
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影响因子：
3
作者：
Cohen KB;Johnson HL;Verspoor K;Roeder C;Hunter LE
通讯作者：
Hunter LE
影响因子：
12.3
作者：
Bard J;Rhee SY;Ashburner M
通讯作者：
Ashburner M
影响因子：
14.9
作者：
Bodenreider, O
通讯作者：
Bodenreider, O
DOI：
10.1093/bioinformatics/btq180
发表时间：
2010-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Björne J;Ginter F;Pyysalo S;Tsujii J;Salakoski T
通讯作者：
Salakoski T
影响因子：
3.7
作者：
Cohen KB;Palmer M;Hunter L
通讯作者：
Hunter L