Dataset search: a survey
Dataset search: a survey
复制标题
DOI：
10.1007/s00778-019-00564-x
复制
发表时间：
2020-01-01
期刊：
影响因子：
4.2
通讯作者：
Groth, Paul
中科院分区：
文献类型：
--
作者：
Chapman, Adriane;Simperl, Elena;Groth, Paul
从数据中产生价值需要能够找到，访问和理解数据集。有许多努力正在鼓励数据共享和重用，从科学出版商要求作者提交数据和手稿到数据市场，开放的数据门户和数据社区。谷歌最近发布了一项数据集搜索服务，允许用户通过关键字查询发现存储在各种在线存储库中的数据。这些发展预示着围绕数据集搜索或检索的新兴研究领域，该领域广泛涵盖有助于将用户数据需求与数据集集合相匹配的框架，方法和工具。在这里，我们调查了研究和商业系统的最新发展状况，并讨论了是什么使数据集搜索成为一个独立的领域，具有独特的挑战和开放的问题。我们从数据集搜索所借鉴的相关领域研究方法和实现，包括信息检索、数据库、以实体为中心的搜索和表格搜索，以确定解决这些问题的可能途径，以及推动该领域向前发展的下一步措施。
Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta-released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems and discuss what makes dataset search a field in its own right, with unique challenges and open questions. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to tackle these questions as well as immediate next steps that will take the field forward.