HCC: Small: An Analytical Framework for Provenance-Rich Social Knowledge Collection

HCC：小型：来源丰富的社会知识收集的分析框架

• 批准号: 1117281
• 负责人: Yolanda Gil
• 金额: $ 49万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117281&HistoricalAwards=false
• 关键词: HCC; Small; Analytical; Framework; Provenance

This project will investigate a new generation of provenance-rich social knowledge collection systems that will greatly improve the ability of people to create online communities of interest and share information. The research will transform the state of the art in social content collection in several important ways. First, social knowledge collection systems will be augmented to support contributors to structure factual content, so that information can be aggregated to answer reasonably interesting albeit simple factual queries. We will build on a semantic wiki framework to allow users to create structured factual content as object-property-value triples. It will not assume pre-defined ontologies, but rather develop algorithms that analyze current content and suggest opportunities for structuring contributions so they can be aggregated to answer simple queries. Second, they will include detailed provenance records that reflect how the content was created, allowing contributors to enter alternative viewpoints and enabling consumers to make quality and trust judgments. The research will include developing algorithms that derive trust metrics from the provenance records, and to allow users to define views on the content based on provenance criteria. It will create novel approaches to propagate trust across content topics and categories and complement existing algorithms that propagate trust in social networks. Third, the systems will proactively guide contributors to invest effort where it is most needed, developing novel algorithms to detect knowledge gaps, and by allowing users to define queries that will be used to drive further contributions.This work has the potential for a broader impact in many areas where social content collection is already widely used, not only in scientific communities but also for societal issues, such as citizen participation in local communities, health, and governance. All these communities would benefit from further structure, provenance models, and guided knowledge collection. Despite their popularity, social content collection sites currently have important limitations. First, because the content has very little structure they cannot aggregate information and answer many simple questions. Second, contributors have uneven expertise and skills and therefore the content is of very varying quality, yet there is no assistance for consumers to tell apart the valuable from the dubious. Third, these sites depend on the initiative of contributors to figure out how the content needs to grow, and there is no systematic analysis to expose knowledge gaps and guide contributors proactively. This research project addresses all three of those issues.

该项目将研究新一代来源丰富的社会知识收集系统，该系统将极大地提高人们创建在线兴趣社区和共享信息的能力。 该研究将从几个重要方面改变社交内容收集的最新技术。首先，社会知识收集系统将得到增强，以支持贡献者构建事实内容，以便可以聚合信息来回答相当有趣但简单的事实查询。我们将构建一个语义 wiki 框架，允许用户将结构化事实内容创建为对象-属性-值三元组。它不会采用预定义的本体，而是开发算法来分析当前内容并提出构建贡献的机会，以便可以将它们聚合起来以回答简单的查询。其次，它们将包括反映内容创建方式的详细出处记录，允许贡献者输入不同的观点，并使消费者能够做出质量和信任判断。该研究将包括开发从出处记录中得出信任指标的算法，并允许用户根据出处标准定义对内容的看法。 它将创建新颖的方法来传播跨内容主题和类别的信任，并补充在社交网络中传播信任的现有算法。 第三，系统将主动引导贡献者在最需要的地方投入精力，开发新颖的算法来检测知识差距，并允许用户定义将用于推动进一步贡献的查询。这项工作有可能产生更广泛的影响在社会内容收集已经广泛使用的许多领域，不仅在科学界，而且还用于解决社会问题，例如公民参与当地社区、健康和治理。所有这些社区都将受益于进一步的结构、起源模型和指导性知识收集。 尽管社交内容收集网站很受欢迎，但目前仍存在重要的局限性。首先，由于内容结构很少，因此无法汇总信息并回答许多简单的问题。 其次，贡献者的专业知识和技能参差不齐，因此内容质量参差不齐，而消费者却无法区分有价值的内容和可疑的内容。第三，这些网站依赖于贡献者的主动性来弄清楚内容需要如何增长，并且没有系统的分析来揭示知识差距并主动指导贡献者。 该研究项目解决了所有这三个问题。