Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
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DOI：
10.26615/978-954-452-044-1_006
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Seid Muhie Yimam;Steffen Remus;Alexander Panchenko;Andreas Holzinger;Chris Biemann
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文献类型：
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作者：
Seid Muhie Yimam;Steffen Remus;Alexander Panchenko;Andreas Holzinger;Chris Biemann
在本文中，我们描述了生物医学领域以实体为中心的信息访问的概念。随着实体识别技术接近可接受的准确性水平，我们提出了文档浏览和搜索的范例，其中对域的实体及其关系进行显式建模，为用户提供收集有关兴趣关系的详尽信息的可能性。我们沿着这些思路描述了三个工作原型：NEW/S/LEAK，是为需要快速概述大量泄露文件集合的调查记者开发的； STORYFINDER，是网页信息的个性化组织者，允许添加实体和关系，并且能够进行个性化信息管理；以及通用语言注释工具WEBANNO的自适应注释功能。我们将讨论使这些工具适应生物医学数据的未来步骤，这取决于最近启动的生物医学知识获取项目。与其他方法的一个关键区别在于，人机学习方法以用户为中心，用户定义和扩展类别，并使系统能够通过反馈和交互进行改进。
In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.