Studying Visual Analytics Support for Interactive Information Retrieval within Complex Search Settings

研究复杂搜索设置中交互式信息检索的视觉分析支持

• 批准号: RGPIN-2017-06446
• 负责人: Hoeber, Orland
• 金额: $ 1.46万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749826
• 关键词: Studying; Visual; Analytics; Support; Interactive

This research addresses a problem that is becoming increasingly important in our modern information-centric lives. As we have become adept at searching the web for factual information, we are increasingly discovering the need to address complex information seeking problems within specialized knowledge repositories. While web search engines support finding single-page answers to focused questions using simple search interfaces, support for complex information seeking tasks is limited. Resolving such problems requires more than just viewing the top few documents in the search results list; the searcher may need to explore among and make sense of a potentially large number of search results, refine the query as the information seeking goal becomes more precise, and synthesize information gleaned from multiple queries and potentially over multiple information seeking sessions. The goal of this research is to study how visual analytics can support interactive information retrieval activities within complex search settings. The resulting “visual search analytics” research will address four problems: (1) analyze and understand the difficulties of undertaking complex search tasks; (2) design visual analytics approaches that support interactive information retrieval and the resolution of complex information seeking problems; (3) develop and iteratively refine prototype systems that implement these designs; and (4) study the benefits and limitations of such approaches. Consider the difficulty of searching within a digital library when one has limited knowledge of their search topic. In such situations, it is common to start with a vague query, and develop knowledge about the topic incrementally as documents are examined. A visual search analytics approach could use machine learning to extract key topics within the search results, and present this information in a visual format so that their relationships could be seen. This would enable the searcher to easily identify a specific interest, interactively refine the query to match this interest, and produce subsequent visualizations that enable the searcher to compare and make sense of the search results in relation to the topics.Building upon and extending the results of my previous research on using visualization to support human-centred search, this research will undertake a systematic study of visual search analytics within three specialized online knowledge repositories: digital libraries, social media, and online encyclopedias. These repositories each feature important differences in textual data and associated metadata, the types of information seeking behaviours searchers employ, and the criteria for considering the search a success. Studying visual search analytics over multiple knowledge repositories and various searcher behaviours will enable the development of a generalized framework for visual search analytics.

这项研究解决了一个问题，在我们以信息为中心的生活中变得越来越重要。随着我们擅长搜索网络以获取事实信息，我们越来越发现需要解决专业知识存储库中寻求问题的复杂信息。尽管Web搜索引擎支持使用简单的搜索接口找到对集中问题的单页答案，但支持复杂信息寻求任务的支持是有限的。解决此类问题不仅需要查看搜索结果列表中的前几个文档；搜索者可能需要探索并理解潜在的大量搜索结果，请在搜索目标变得更加精确时完善查询，并合成从多个查询中收集的信息，并可能在多个信息寻求会话中收集。这项研究的目的是研究视觉分析如何支持复杂搜索设置中的交互式信息检索活动。由此产生的“视觉搜索分析”研究将解决四个问题：（1）分析并了解执行复杂搜索任务的困难； （2）设计视觉分析方法，支持交互式信息检索和解决问题的复杂信息的解决； （3）开发和迭代完善的原型系统，以实现这些设计； （4）研究这种方法的益处和局限性。当一个人对搜索主题的了解有限时，请考虑在数字库中搜索的困难。在这种情况下，通常是从投票查询开始，并在检查文档时会逐步发展有关该主题的知识。视觉搜索分析方法可以使用机器学习在搜索结果中提取关键主题，并以视觉格式提供此信息，以便可以看到它们的关系。这将使搜索者能够轻松地确定特定的兴趣，从互动地完善查询以匹配这一兴趣，并产生随后的可视化，使搜索者能够比较并了解与主题相关的搜索结果。建立在我先前的研究中，并扩展了我先前对可视化搜索的研究的研究结果，该研究将在线搜索范围内的研究介绍：在线搜索范围：在线搜索媒体：在线搜索介绍，在线搜索范围内，在线搜索范围内，在线搜索范围内，在线搜索范围内的数字介绍，在线搜索范围内的数字介绍，在线搜索范围内的数字化分析，在视觉上介绍了可视化分析，以范围内的专业化范围内的专业化分析，以实现视觉分析，和在线百科全书。这些存储库都在文本数据和相关元数据，寻求行为搜索者员工的信息类型以及考虑搜索成功的标准中都有重要差异。在多个知识存储库和各种搜索者行为上研究视觉搜索分析将使视觉搜索分析的广义框架开发。