面向智能响应的在线异构生物数据库声明性查询映射策略

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
H. Jamil, Kallol Naha
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引用次数: 0

摘要

Alexa和Siri的出现,以及最近OpenAI的Chat-GPT的出现,提出了一个问题,即在没有最终用户积极参与代码编写过程的情况下,是否也可以计算特定的生物查询。虽然已经取得了进展,但目前的生物数据库查询体系结构仍然假设生物学家具有一定程度的计算能力和对数据库底层网络的重要结构意识,如果不是主动编写代码的话。鉴于生物数据库是高度分布式和异构的,而且大多数不符合FAIR标准,因此,要准确地编写查询并有意义地执行查询,就必须具备大量数据集成方面的专业知识。本文介绍了一种灵活智能的查询改写助手Needle,作为在线生物数据库自然语言查询接口的后端查询执行引擎。Needle利用了一个名为BioStar的数据模型,该模型利用了一个名为图式图的元知识库,将自然语言查询映射到相关数据库和生物学概念。使用BioStar实现Needle是本文的重点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping Strategies for Declarative Queries over Online Heterogeneous Biological Databases for Intelligent Responses
The emergence of Alexa and Siri, and more recently, OpenAI's Chat-GPT, raises the question whether ad hoc biological queries can also be computed without end-users' active involvement in the code writing process. While advances have been made, current querying architectures for biological databases still assume some degree of computational competence and significant structural awareness of the underlying network of databases by biologists, if not active code writing. Given that biological databases are highly distributed and heterogeneous, and most are not FAIR compliant, a significant amount of expertise in data integration is essential for a query to be accurately crafted and meaningfully executed. In this paper, we introduce a flexible and intelligent query reformulation assistant, called Needle, as a back-end query execution engine of a natural language query interface to online biological databases. Needle leverages a data model called BioStar that leverages a meta-knowledgebase, called the schema graph, to map natural language queries to relevant databases and biological concepts. The implementation of Needle using BioStar is the focus of this article.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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