[Adapt] Fwd: 23日10点学术报告

[Kenny Zhu]

Hi all,

Pls go to this talk. Much of our work is related to Daisys work.

Kenny


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<div>-------- Original message --------</div><div>From: chen-zx <chen-zx at cs.sjtu.edu.cn> </div><div>Date:2014/04/22  09:50  (GMT+08:00) </div><div>To: all at cs.sjtu.edu.cn </div><div>Subject: 23日10点学术报告 </div><div>
</div>各位老师好!

来自佛罗里达大学计算机信息科学与工程系的一位助理教授Wang Zhe将于23日（明天）上午10点在404教室做一个题为“ Large 
Probabilistic Knowledge Base 
Systems”的学术报告，诚邀有兴趣的老师和同学前来参加！（麻烦老师们转发给学生，谢谢！）以下为讲座具体内容：

Title: Large Probabilistic Knowledge Base Systems
Speaker: Zhe Wang
Time: 10:00-11:30, 23rd April
Venue: SEIEE 3-404
Abstract: Keyword search engines have been the state-of-the-art 
information retrieval tool over large text corpora for two decades. To 
date, most search engines have little understanding that keywords and 
documents refer to entities and relations in real-life. Better search 
results and experience can be achieved by understanding entities and 
relations in documents as well as in queries. A knowledge base (KB) 
containing relevant entities and relations should be the backbone of any 
application that is fueled by text. Given a large amount of text data, a 
system is needed that can automatically construct a knowledge base using 
statistical machine learning (SML) methods, manage the uncertainty 
inherent in the extracted knowledge, and maintain them over time.
In this talk, I first summarize the major results from BayesStore, a 
probabilistic database system that natively supports SML models and 
various inference algorithms to perform query-driven knowledge 
extraction from text and probabilistic query processing over uncertain 
extractions. Results show that BayesStore can significantly improve 
performance and answer quality for queries over unstructured text.
With BayesStore as a foundation, I propose to build a probabilistic 
knowledge base (ProbKB) system with a deep integration of the SML 
methods with scalable data processing frameworks. A ProbKB system should 
be designed to support various aspects in the life of a knowledge base 
(KB) including KB extraction, expansion, 
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