Recently, keyword search on Knowledge Graphs (KGs) becomes popular. Typical keyword search approaches aim at finding a concise subgraph from a KG, which can reflect a close relationship among all input keywords. The connection paths between keywords are selected in a way that leads to a result subgraph with a better semantic score. However, such a result may not meet user information need because it relies on the scoring function to decide what keywords to link closer. Therefore, such a result may miss close connections among some keywords on which users intend to focus. In this paper, we propose a parallel keyword search engine, called RAKS. It allows users to specify a query as two sets of keywords, namely central keywords and marginal keywords. Specifically, central keywords are those keywords on which users focus more. Their relationships are desired in the results. Marginal keywords are those less focused keywords. Their connections to the central keywords are desired. In addition, they provide additional information that helps discover better results in terms of user intents. To improve the efficiency, we propose novel weighting and scoring schemes that boost the parallel execution during search while retrieving semantically relevant results. We conduct extensive experiments to validate that RAKS can work efficiently and effectively on open KGs with large size and variety.

中文翻译：

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并行知识图谱上的高效径向模式关键字搜索

最近，知识图谱（KG）上的关键字搜索变得流行起来。典型的关键字搜索方法旨在从 KG 中找到一个简洁的子图，它可以反映所有输入关键字之间的密切关系。关键字之间的连接路径以导致具有更好语义分数的结果子图的方式选择。但是，这样的结果可能无法满足用户信息需求，因为它依赖于评分函数来决定哪些关键字链接得更紧密。因此，这样的结果可能会错过一些用户打算关注的关键字之间的密切联系。在本文中，我们提出了一个并行关键字搜索引擎，称为 RAKS。它允许用户将查询指定为两组关键字，即中心关键字和边缘关键字。具体来说，中心关键词是用户更关注的关键词。结果中需要它们的关系。边缘关键字是那些不太集中的关键字。需要它们与中心关键字的联系。此外，它们提供了额外的信息，有助于根据用户意图发现更好的结果。为了提高效率，我们提出了新颖的加权和评分方案，以在检索语义相关结果的同时促进搜索期间的并行执行。我们进行了广泛的实验来验证 RAKS 可以在大尺寸和种类的开放 KG 上高效有效地工作。我们提出了新颖的加权和评分方案，可在检索语义相关结果的同时促进搜索期间的并行执行。我们进行了广泛的实验来验证 RAKS 可以在大尺寸和种类的开放 KG 上高效有效地工作。我们提出了新颖的加权和评分方案，可在检索语义相关结果的同时促进搜索期间的并行执行。我们进行了广泛的实验来验证 RAKS 可以在大尺寸和种类的开放 KG 上高效有效地工作。