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Towards Exploratory Relationship Search: A Clustering-Based Approach

机译:探索性关系搜索:一种基于聚类的方法

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Searching and browsing relationships between entities is an important task in many domains. RDF facilitates searching by explicitly representing a relationship as a path in a graph with meaningful labels. As the Web of RDF data grows, hundreds of relationships can be found between a pair of entities, even under a small length constraint and within a single data source. To support users with various information needs in interactively exploring a large set of relationships, existing efforts mainly group the results into faceted categories. In this paper, we practice another direction of exploratory search, namely clustering. Our approach automatically groups relationships into a dynamically generated hierarchical clustering according to their schematic patterns, which also meaningfully label these clusters to effectively guide exploration and discovery. To demonstrate it, we implement our approach in the RelClus system based on DBpedia, and conduct a preliminary user study as well as a performance testing.
机译:在许多领域中,搜索和浏览实体之间的关系是一项重要的任务。 RDF通过在图形中使用有意义的标签将关系显式表示为路径来促进搜索。随着RDF数据网络的发展,即使在很小的长度约束下,也可以在单个数据源中找到一对实体之间的数百种关系。为了支持具有各种信息需求的用户以交互方式探索大量关系,现有的工作主要将结果分为多方面。在本文中,我们实践了探索性搜索的另一个方向,即聚类。我们的方法根据关系图的示意图将关系自动分组为动态生成的层次聚类,这也有意义地标记了这些聚类以有效地指导探索和发现。为了证明这一点,我们在基于DBpedia的RelClus系统中实现了我们的方法,并进行了初步的用户研究以及性能测试。

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