Literature-Based Discovery: Beyond the ABCs
Literature-Based Discovery: Beyond the ABCs
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DOI：
10.1002/asi.21599
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发表时间：
2012-02-01
影响因子：
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通讯作者：
Smalheiser, Neil R.
中科院分区：
文献类型：
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作者：
Smalheiser, Neil R.
基于文献的发现 (LBD) 是指一种特定类型的文本挖掘，旨在识别隐含且未明确陈述的重要断言，并且通过并置（通常是大量）文档来检测这些断言。在这篇评论中，我将简要概述 LBD 的过去和现在，并提出未来十年的一些新方向。流行的 ABC 模型并没有“错误”；相反，它是正确的。然而，它只是有助于开发下一代 LBD 工具的几种不同类型的模型之一。也许最迫切的需要是开发一系列客观的基于文献的兴趣度测量，可以为不同类型的科学研究定制LBD系统的输出。
Literature-based discovery (LBD) refers to a particular type of text mining that seeks to identify nontrivial assertions that are implicit, and not explicitly stated, and that are detected by juxtaposing (generally a large body of) documents. In this review, I will provide a brief overview of LBD, both past and present, and will propose some new directions for the next decade. The prevalent ABC model is not "wrong"; however, it is only one of several different types of models that can contribute to the development of the next generation of LBD tools. Perhaps the most urgent need is to develop a series of objective literature-based interestingness measures, which can customize the output of LBD systems for different types of scientific investigations.