Variable Detection, Interlinking and Summarization (VADIS)

变量检测、互连和汇总 (VADIS)

• 批准号: 448491925
• 负责人: Professor Dr. Kai Eckert
• 金额: --
• 依托单位: Abteilung Wissenstechnologien für Sozialwissenschaften (WTS)
• 依托单位国家: 德国
• 项目类别: Research data and software (Scientific Library Services and Information Systems)
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/448491925?language=en
• 关键词: Variable; Detection; Interlinking; Summarization; VADIS

Nowadays there is a growing trend in many scientific disciplines to support researchers by providing enhanced information access through linking of publications and underlying datasets. Open Science encourages scientific practices in which all research data is interlinked and contextualized to enhance reproducibility and reusability of research results. Ideally, publications that report on a result of an empirical study should thus contain a direct link to the cited dataset and lead the reader directly to the research data that underlies the publication. However, in practice, standards for referencing between primary text and the cited data and its variables are often missing. A recent user study conducted by GESIS reveals that researchers would considerably benefit from increased linking and semantic annotation of scientific publications. In addition, researchers also demand that data citations should include information at the right granular level of the cited data, thus facilitating the identification and verification of the part of data that actually supports a specific claim. Improving access to scientific publications along the FAIR best practices also requires semantic indexing of texts with salient entities and specific variables that make up the focus of the study – requirements which are rarely addressed today.The key vision behind VADIS is to allow for searching und using survey variables in context and thereby help to increase the reproducibility of research results. We achieve this by combining text mining techniques and semantic web technologies that identify and exploit links between publications, their topics, and the specific variables that are covered in the surveys. These semantic links in scientific texts build the basis for the development of applications to give users better access to scientific literature such as passage search, summarization, and information retrieval. To achieve this, we will analyze and link variables in context by identifying references to survey variables within the full text of research literature, creating semantic links based on these references and making the resulting data available as Linked Open Data. Next, we will develop data-driven profiles of survey variable on the basis of both context-independent and context-dependent metrics. Finally, we will improve the access to survey and literature by providing information on variables from surveys, the developed metrics as well as textual summaries of linked literature. As a result of this, our project will be able to provide improved access to research literature in the social sciences based on the seamless integration within existing infrastructures. To quantify the effectiveness of our framework we design several use case scenarios for a target group of researchers that will be implemented as interfaces for exploration and research. The improvements on information access from experts will be thoroughly investigated in a user study.

如今，在许多科学学科中，通过链接出版物和基础数据集来提供增强的信息访问，以支持研究人员，这是一个日益增长的趋势。开放科学鼓励科学实践，其中所有研究数据都是相互关联和情境化的，以提高研究结果的可重复性和可重用性。因此，理想情况下，报告实证研究结果的出版物应该包含与引用数据集的直接链接，并将读者直接引导到出版物背后的研究数据。然而，在实践中，原始文本和引用数据及其变量之间的引用标准往往缺失。GESIS最近进行的一项用户研究表明，研究人员将从科学出版物的链接和语义注释的增加中受益匪浅。此外，研究人员还要求数据引用应包括所引用数据的正确粒度级别的信息，从而便于识别和验证实际支持特定声明的数据部分。沿着FAIR最佳实践改善对科学出版物的访问还需要对具有突出实体和特定变量的文本进行语义索引，这些实体和变量构成了研究的重点--这些要求今天很少得到满足。VADIS背后的关键愿景是允许在上下文中搜索和使用调查变量，从而有助于提高研究结果的可重复性。我们通过结合文本挖掘技术和语义网技术来实现这一目标，这些技术可以识别和利用出版物、其主题和调查中所涵盖的特定变量之间的链接。科学文本中的这些语义链接为应用程序的开发奠定了基础，使用户能够更好地访问科学文献，如段落搜索，摘要和信息检索。为了实现这一目标，我们将通过识别研究文献全文中对调查变量的引用来分析和链接变量，基于这些引用创建语义链接，并将所得数据作为链接开放数据提供。接下来，我们将在上下文无关和上下文相关指标的基础上开发调查变量的数据驱动配置文件。最后，我们将通过提供有关调查变量的信息，开发的指标以及链接文献的文本摘要来改善对调查和文献的访问。因此，我们的项目将能够在现有基础设施的无缝集成的基础上，提供更好的社会科学研究文献。为了量化我们的框架的有效性，我们设计了几个用例场景的目标群体的研究人员，将实现为接口的探索和研究。将在一项用户研究中彻底调查专家信息获取方面的改进。