MagicCube: choosing the best snippet for each aspect of an entity

• Authors: Yexin Wang;Li Zhao;Yan Zhang
• Venue: Proceedings of the 18th ACM conference on Information and knowledge management
• Year: 2009

Wikis are currently used in business to provide knowledge management systems, especially for individual organizations. However, building wikis manually is a laborious and time-consuming work. To assist founding wikis, we propose a methodology in this paper to automatically select the best snippets for entities as their initial explanations. Our method consists of two steps. First, we focus on extracting snippets from a given set of web pages for each entity. Starting from a seed sentence, a snippet grows up by adding the most relevant neighboring sentences into itself. The sentences are chosen by the Snippet Growth Model, which employs a distance function and an influence function to make decisions. Secondly, we pick out the best snippet for each aspect of an entity. The combination of all the selected snippets serves as the primary description of the entity. We present three ever-increasing methods to handle selection process. Experimental results based on a real data set show that our proposed method works effectively in producing primary descriptions for entities such as employee names.