An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks

1. Systematic review, meta-analysis, and other forms of evidence synthesis are critical to strengthen the evidence base concerning conservation issues and to answer ecological and evolutionary questions. Synthesis lags behind the pace of scientific publishing, however, due to time and resource costs which partial automation of evidence synthesis tasks could reduce. Additionally, current methods of retrieving evidence for synthesis are susceptible to bias towards studies with which researchers are familiar. In fields that lack standardized terminology encoded in an ontology, including ecology and evolution, research teams can unintentionally exclude articles from the review by omitting synonymous phrases in their search terms. 2. To combat these problems, we developed a quick, objective, reproducible method for generating search strategies that uses text mining and keyword co-occurrence networks to identify the most important terms for a review. The method reduces bias in search strategy...1. 系统综述（Systematic review）、荟萃分析（meta-analysis）及其他形式的证据合成（evidence synthesis），对于强化保护议题的证据基础、解答生态学与进化生物学问题至关重要。然而，受限于时间与资源成本，证据合成的推进速度落后于科学出版的节奏，而证据合成任务的部分自动化可有效降低此类成本。此外，当前用于证据合成的检索方法，易偏向研究者熟悉的研究，从而引入检索偏倚。在生态学、进化生物学等尚未通过本体论（ontology）编码标准化术语的领域，研究团队若在检索词中遗漏同义短语，可能会无意中将相关文献排除在综述之外。 2. 为解决上述问题，本研究开发了一种快速、客观且可复现的检索策略生成方法，该方法借助文本挖掘（text mining）与关键词共现网络（keyword co-occurrence networks）识别综述所需的核心术语。该方法可降低检索策略构建过程中的偏倚……