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Curious Cat-Mobile, Context-Aware Conversational Crowdsourcing Knowledge Acquisition

机译：好奇的猫移动，上下文感知的对话众包知识获取

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摘要

Scaled acquisition of high-quality structured knowledge has been a longstanding goal of Artificial Intelligence research. Recent advances in crowdsourcing, the sheer number of Internet and mobile users, and the commercial availability of supporting platforms offer new tools for knowledge acquisition. This article applies context-aware knowledge acquisition that simultaneously satisfies users' immediate information needs while extending its own knowledge using crowdsourcing. The focus is on knowledge acquisition on a mobile device, which makes the approach practical and scalable; in this context, we propose and implement a new KA approach that exploits an existing knowledge base to drive the KA process, communicate with the right people, and check for consistency of the user-provided answers. We tested the viability of the approach in experiments using our platform with real users around the world, and an existing large source of common-sense background knowledge. These experiments show that the approach is promising: the knowledge is estimated to be true and useful for users 95% of the time. Using context to proactively drive knowledge acquisition increased engagement and effectiveness (the number of new assertions/day/user increased for 175%). Using pre-existing and newly acquired knowledge also proved beneficial.
机译：大规模获取高质量的结构化知识一直是人工智能研究的长期目标。众包的最新进展，大量的Internet和移动用户以及支持平台的商业可用性为知识获取提供了新的工具。本文应用了上下文相关的知识获取，该知识获取同时满足了用户的即时信息需求，同时使用众包扩展了自己的知识。重点是在移动设备上获取知识，这使得该方法实用且可扩展。在这种情况下，我们提出并实施一种新的KA方法，该方法利用现有的知识库来推动KA流程，与合适的人进行交流并检查用户提供的答案是否一致。我们使用我们的平台在全球范围内的真实用户和现有的大量常识背景知识来源中进行了实验，测试了该方法的可行性。这些实验表明该方法很有希望：据估计，该知识是真实的，并且对用户有95％的时间有用。使用上下文主动推动知识获取可以提高参与度和有效性（每天/用户的新断言数量增加了175％）。使用现有的和新获得的知识也被证明是有益的。

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