论文标题
在知识图上的有效径向模式关键字搜索并行
Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel
论文作者
论文摘要
最近，知识图（kgs）上的关键字搜索变得流行。典型的关键字搜索方法旨在从kg中找到简洁的子图，这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一种导致结果子图的方式选择具有更好的语义分数。但是，这样的结果可能无法满足用户信息的需求，因为它依赖于评分功能来决定要链接的关键字。因此，这样的结果可能会错过某些打算关注的关键字之间的密切联系。在本文中，我们提出了一个名为Raks的并行关键字搜索引擎。它允许用户指定查询为两组关键字，即中心关键字和边缘关键字。具体而言，中心关键字是用户更多关注的关键字。他们的关系在结果中是必需的。边缘关键字是那些不太关注的关键字。需要他们与中心关键字的连接。此外，它们还提供了其他信息，可帮助您从用户意图方面发现更好的结果。为了提高效率，我们提出了新颖的加权和评分方案，从而在搜索过程中提高了平行执行，同时检索了语义相关的结果。我们进行了广泛的实验，以验证RAKS可以在大尺寸和多样性的开放式公园内有效地工作。
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.
