AMETHYST:用于挖掘和探索异构数据的主题层次结构的系统

Marina Danilevsky, Chi Wang, Fangbo Tao, Son Nguyen, Gong Chen, Nihit Desai, Lidan Wang, Jiawei Han
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引用次数: 9

摘要

在这个演示中,我们展示了AMETHYST,一个用于探索和分析由异构信息网络(HIN)构建的主题层次结构的系统。HINs由多种类型的实体和链接组成,在现实世界中非常常见。许多具有文本组件,因此可以从网络数据集中主题的高质量分层组织中受益。通过将主题组织到层次结构中,AMETHYST有助于理解本体上下文中的搜索结果,并在不同粒度上解释实体的相关性。自动构建的主题层次结构反映了特定于领域的本体,与多种类型的链接实体交互,并且可以针对自由文本和OLAP查询进行定制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data
In this demo we present AMETHYST, a system for exploring and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed topical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.
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