异构图上语义接近搜索的交互式路径嵌入Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs |
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课程网址: | http://videolectures.net/kdd2018_liu_interactive_embedding/ |
主讲教师: | Zemin Liu |
开课单位: | 浙江大学 |
开课时间: | 2018-11-23 |
课程语种: | 英语 |
中文简介: | 异构图上的语义邻近搜索是一项重要任务,对许多应用都很有用。它的目的是测量异构图上两个节点之间的接近度,即给定的语义关系。先前的工作通常试图通过连接查询对象和目标对象的路径来度量语义接近度。尽管这种基于路径的方法取得了成功,但它们通常以弱耦合的方式对路径进行建模,这忽略了路径之间的丰富交互。在本文中,我们引入了一种新的交互路径概念,以对查询对象和目标对象之间的多条路径之间的相互依赖性进行建模。然后,我们提出了一种交互式路径嵌入(IPE)模型,该模型学习所得交互式路径结构的低维表示,以进行接近度估计。我们用四种不同类型的异构图对七种关系进行了实验,并表明我们的模型优于最先进的基线。 |
课程简介: | Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the interdependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines. |
关 键 词: | 语义邻近搜索; 度量语义接近度; 交互式路径结构的低维表示 |
课程来源: | 视频讲座网 |
数据采集: | 2023-02-01:cyh |
最后编审: | 2023-02-01:cyh |
阅读次数: | 37 |