在生物医学数据库系统中实现基于本体的语义查询。

IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shuai Zheng, Fusheng Wang, James Lu
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引用次数: 0

摘要

生物医学数据库通常标注有本体概念,但目前还缺乏工具来简化生物医学数据库的集成和基于本体的语义查询。我们的目标是在本体资源库和语义注释数据库之间提供一个中间层,以支持在数据库中直接使用具有表现力的标准数据库查询语言进行语义查询。我们开发了一个语义查询引擎,提供语义推理和查询处理,并将查询转化为 NCBO BioPortal 上的本体库操作。语义操作符在数据库中实现,作为用户定义的函数扩展到数据库引擎,因此语义查询可直接用标准数据库查询语言(如SQL和XQuery)指定。系统提供缓存管理,以提高查询性能。该系统具有很强的适应性,可通过简单的定制支持不同的本体。我们已将 DBOntoLink 系统作为开源软件实现,它支持 BioPortal 上托管的主要本体。DBOntoLink 支持一系列基于本体的通用语义操作,并与 IBM DB2 数据库管理系统完全集成。该系统已与现有的生物医学数据库一起部署和评估,用于管理和查询图像注释和标记(AIM)。我们的性能研究证明了语义查询的高表达能力和查询的高效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enabling Ontology Based Semantic Queries in Biomedical Database Systems.

Enabling Ontology Based Semantic Queries in Biomedical Database Systems.

There is a lack of tools to ease the integration and ontology based semantic queries in biomedical databases, which are often annotated with ontology concepts. We aim to provide a middle layer between ontology repositories and semantically annotated databases to support semantic queries directly in the databases with expressive standard database query languages. We have developed a semantic query engine that provides semantic reasoning and query processing, and translates the queries into ontology repository operations on NCBO BioPortal. Semantic operators are implemented in the database as user defined functions extended to the database engine, thus semantic queries can be directly specified in standard database query languages such as SQL and XQuery. The system provides caching management to boosts query performance. The system is highly adaptable to support different ontologies through easy customizations. We have implemented the system DBOntoLink as an open source software, which supports major ontologies hosted at BioPortal. DBOntoLink supports a set of common ontology based semantic operations and have them fully integrated with a database management system IBM DB2. The system has been deployed and evaluated with an existing biomedical database for managing and querying image annotations and markups (AIM). Our performance study demonstrates the high expressiveness of semantic queries and the high efficiency of the queries.

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来源期刊
International Journal of Semantic Computing
International Journal of Semantic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
1.70
自引率
12.50%
发文量
39
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