本体论在生命科学中的应用基于文本的资源组织

Giulia Panzarella , Pierangelo Veltri , Stefano Alcaro
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引用次数: 2

摘要

本体用于支持对涵盖领域相关信息的大量数据库的访问。通过在分层概念定义中使用结构化文本和描述,可以访问异构性和不同的语义。我们对生命科学(LS)相关的本体感兴趣,包括来自分子生物学,生物信息学,物理学,化学,医学和其他相关领域的组件。本体包括:(i)术语连接,(ii)核心概念的识别,(iii)数据管理,(iv)知识分类和集成以收集关键信息。本体在导航LS术语时可能非常有用。本文探讨了一些现有的生物医学本体和框架。它描述了最常见的本体开发环境(ODE): prot、Topbraid Composer、Ontostudio、Fluent Editor、VocBench、Swoop和Obo-edit,用于从文本科学资源中为LS计划创建本体。本文还从可用性、可扩展性、稳定性、集成、文档化和原创性等方面对本体方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using ontologies for life science text-based resource organization

Ontologies are used to support access to a multitude of databases that cover domains relevant information. Heterogeneity and different semantics can be accessed by using structured texts and descriptions in a hierarchical concept definition. We are interested in Life Sciences (LS) related ontologies including components taken from molecular biology, bioinformatics, physics, chemistry, medicine and other related areas. An Ontology comprises: (i) term connections, (ii) the identification of core concepts, (iii) data management, (iv) knowledge classification and integration to collect key information. An ontology may be very useful in navigating through LS terms. This paper explores some available biomedical ontologies and frameworks. It describes the most common ontology development environments (ODE): Protégé, Topbraid Composer, Ontostudio, Fluent Editor, VocBench, Swoop and Obo-edit, to create ontologies from textual scientific resources for LS plans. It also compares ontology methodologies in terms of Usability, Scalability, Stability, Integration, Documentation and Originality.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
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15 days
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