Detection of Entity Properties in Content Stream
Date Issued
2014
Date
2014
Author(s)
Li, Qing-Cheng
Abstract
World knowledge varies with time, but the change of knowledge about an entity often waits for a long time before a human editor update it in knowledge base (KB). How to accelerate the update of KB is an important problem, it’s also called knowledge base acceleration (KBA).
In this paper, we propose a method that detects entity’s properties in content stream efficiently and effectively base on patterns. The detection process has three phases including pattern selection phase, pattern matching phase and property disambiguation phase. pattern quality, reliability and ambiguity are three major issues in the process.
The experimental results show the impact of patterns’ confidence value,
reliability and ambiguity degree. We found that using the entity type information and naive bayes classifier improve the performance of the detection system.
Detection of entity’s properties filters documents from content stream. It’s
helpful for human editors to use the information in those documents to update the KB.
Subjects
知識庫加速
樣式比對
實體特性偵測
Type
thesis
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