Curation at scale: Integrating AI into community curation

项目摘要 生物学知识库是研究人员的重要资源，通过以下方式加速科学发现： 提供人工管理的、机器可读的数据集合。然而，聚合和手动策展 生物数据的处理是一个劳动密集型的过程，几乎完全依赖于专业的生物处理人员。两 已经提出了一些方法来帮助解决这个问题：自然语言处理（NLP）;文本挖掘（TM） 和机器学习（ML））和研究人员的参与（社区策展）。然而，这两个 这些方法本身就足以解决生物固化过程中提高效率的关键需求。我们 这些挑战的解决方案是一个NLP增强的社区策展门户，作者策展到知识库 （承认）。ACKnowledge系统目前已在C.优雅的文学，夫妇 统计方法和文本挖掘算法，以增强研究文章的社区管理。我们提出 通过将其他物种纳入我们的管道来加强和扩大ACKnowledge， 复杂的机器学习模型，并提供高级实体和概念提取， 作者详细介绍此外，我们将开发一个作者策展门户（ACP），让作者轻松地 上传和整理自己的文档。综合考虑，这些增强功能将使我们能够最大限度地 社区策展工作，利用作者在生物学多个领域的专业知识，同时 尽可能多地支持作者进行人工智能辅助的策展。这种相互作用不会改善 只有知识库的内容，但人工智能的方法本身，因为我们将收到宝贵的反馈，我们的 模型通过开发一个作者策展门户网站，我们将进一步授权作者参与策展 处理知识库，并提醒知识库注意能够并且应该根据 FAIR（Findable，Interoperable，Reusable）数据原则。

Project Summary Biological knowledgebases are a critical resource for researchers and accelerate scientific discoveries by providing manually curated, machine-readable data collections. However, the aggregation and manual curation of biological data is a labor-intensive process that relies almost entirely on professional biocurators. Two approaches have been advanced to help with this problem: natural language processing (NLP; text mining (TM) and machine learning (ML)) and engagement of researchers (community curation). However, neither of these approaches alone is sufficient to address the critical need for increased efficiency in the biocuration process. Our solution to these challenges is an NLP-enhanced community curation portal, Author Curation to Knowledgebase (ACKnowledge). The ACKnowledge system, currently implemented for the C. elegans literature, couples statistical methods and text mining algorithms to enhance community curation of research articles. We propose to strengthen and expand ACKnowledge by including other species into our pipeline, incorporating more sophisticated machine learning models, and presenting sentence-level entity and concept extraction for more detailed author curation. In addition, we will develop an Author Curation Portal (ACP) to allow authors to easily upload and curate their own documents. Taken together, these enhancements will allow us to maximize community curation efforts by leveraging author expertise in multiple areas of biology, while at the same time supporting authors with as much AI-assisted curation as possible. This reciprocal interaction will improve not only the content of knowledgebases, but the AI methods themselves, as we will receive valuable feedback on our models. By developing an Author Curation Portal, we will further empower authors to participate in the curation process and alert knowledgebases to key information that can, and should, be readily discoverable in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.