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GraphCharter: Combining browsing with query to explore large semantic graphs

机译:GraphCharter:将浏览与查询结合以探索大型语义图

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Large scale semantic graphs such as social networks and knowledge graphs contain rich and useful information. However, due to combined challenges in scale, density, and heterogeneity, it is impractical for users to answer many interesting questions by visual inspection alone. This is because even a semantically simple question, such as which of my extended friends are also fans of my favorite band, can in fact require information from a non-trivial number of nodes to answer. In this paper, we propose a method that combines graph browsing with query to overcome the limitation of visual inspection. By using query as the main way for information discovery in graph exploration, our “query, expand, and query again” model enables users to probe beyond the visible part of the graph and only bring in the interesting nodes, leaving the view clutter-free. We have implemented a prototype called GraphCharter and demonstrated its effectiveness and usability in a case study and a user study on Freebase knowledge graph with millions of nodes and edges.
机译:诸如社交网络和知识图之类的大规模语义图包含丰富而有用的信息。然而,由于在规模,密度和异质性方面的综合挑战,用户仅凭视觉检查来回答许多有趣的问题是不切实际的。这是因为,即使是一个语义上简单的问题(例如,我的哪些扩展朋友也是我最喜欢的乐队的粉丝),实际上也可能需要大量节点的信息来回答。在本文中,我们提出了一种将图浏览与查询结合起来的方法,以克服视觉检查的局限性。通过使用查询作为图探索中信息发现的主要方式,我们的“查询,扩展和再次查询”模型使用户可以探查图的可见部分之外,而仅引入有趣的节点,从而使视图整洁。我们已经实现了一个名为GraphCharter的原型,并在具有数百万个节点和边的Freebase知识图的案例研究和用户研究中展示了其有效性和可用性。

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