AMETHYST: A System for Mining and Exploring Topical Hierarchies in Information Networks
- Marina Danilevsky ,
- Chi Wang ,
- Fangbo Tao ,
- Son Nguyen ,
- Gong Chen ,
- Nihit Desai ,
- Lidan Wang ,
- Jiawei Han
Proceeding of 2013 ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Published by ACM – Association for Computing Machinery
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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