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

该项目将研究新一代来源丰富的社会知识收集系统，这将大大提高人们创建在线兴趣社区和共享信息的能力。 这项研究将在几个重要方面改变社会内容收集的艺术状态。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，这样信息就可以被聚合，以回答相当有趣但简单的事实查询。我们将建立一个语义维基框架，允许用户创建结构化的事实内容作为对象-属性-值三元组。它不会假设预定义的本体，而是开发算法来分析当前内容并建议结构化贡献的机会，以便它们可以被聚合以回答简单的查询。其次，它们将包括详细的出处记录，反映内容是如何创建的，允许贡献者输入其他观点，并使消费者能够做出质量和信任判断。该研究将包括开发算法，从出处记录中获得信任度量，并允许用户根据出处标准定义对内容的看法。 它将创造新的方法来传播跨内容主题和类别的信任，并补充现有的算法，传播信任的社交网络。 第三，该系统将主动引导贡献者在最需要的地方投入精力，开发新的算法来检测知识差距，并允许用户定义用于推动进一步贡献的查询。这项工作有可能在许多领域产生更广泛的影响，这些领域已经广泛使用社交内容收集，不仅在科学界，而且在社会问题上，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，起源模型和指导知识收集。 尽管它们很受欢迎，但社交内容收集网站目前有重要的局限性。首先，因为内容结构非常少，他们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，因此内容的质量参差不齐，但消费者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确定内容需要如何增长，并且没有系统的分析来暴露知识差距并主动指导贡献者。 这个研究项目解决了这三个问题。

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.