Entity Rank Searching Entities Directly and Holistically Tao
- Slides: 28
Entity. Rank: Searching Entities Directly and Holistically - Tao Cheng, Xifeng Yan, Kevin Chen-Chuan Chang CS Department, UIUC Presented By: Md. Abdus Salam Ph. D Student CSE Department, UTA
Motivating Scenario Customer service phone number of
Search on Amazon?
Search on Google?
Many many Similar Cases �The email of Luis Gravano? �What profs are doing databases at UIUC? �The papers and presentations of ICDE 2007? �Due date of SIGMOD 2008? �Sale price of “Canon Power. Shot A 400”? �“Hamlet” books available at bookstores? Often times, we are looking for data entities, e. g. emails, dates, prices, etc, not pages.
What you search is not what you want.
From pages to entities Traditional Search Entity Search Results Keywords Entities Support
Concretely, what is meant by Entity Search?
Entity Search Problem: Given: Input: Keywords & Entities (optionally with a pattern) E. g. Amazon Customer Service #phone 0. 90 0. 80 Output: Ranked Entity Tuples 0. 60 … … 9
Challenge: How to rank Entities? 10
Characteristics I: Contextual -Utilize Entities’ Surrounding Context Content
Characteristics II: Uncertain -Extractions are non”prefect”
Characteristics III: Holistic -Many evidences from multiple sources
Characteristics IV: Discriminative - Web Pages are of Varying Quality
Characteristics V: Associative -Tell True Associations from Accidental Example: Finding Prof. Luis Gravano’s Email Observation: info@acm. org appears very frequently with keywords “Luis”, “Gravano” However, such association is only accidental as info@acm. org appears on many pages.
Entity. Rank: The Impression Model 0. 90 Validation Layer: Hypothesis Testing 0. 80 Tireless Observer ? ? . . ? ? 0. 60 … Recognition Layer: Local Assessment Access Layer: Global Aggregation …
Recognition Layer: Local Assessment Input: L 1 L 2 Output: Contextual Uncertain Holistic Discriminative Associative 17
Access Layer: Global Aggregation Input: Contextual Uncertain Holistic Discriminative Output: Holistic Discriminative Associative 18
Validation Layer: Hypothesis Testing Input: Virtual Collection E’ over D’ Collection E over D randomize Output: Contextual Uncertain Holistic Discriminative Associative 19
Entity. Rank: The Scoring Function Validation Global Aggregation Local Recognition
Query Processing Hypothesis Test Result 800 -202 -7575: p 4 800 -322 -9266: p 5 Aggregation wv 800 -202 -7575: p 2 800 -202 -7575: p 1 800 -322 -9266: p 3 Sort-merge Join Doc Posting Doc d 1 d 3 d 6 d 7 d 9 d 3 11 d 5 66 d 7 8, 24 d 3 12 d 7 9 d 8 44 d 2 d 3 d 7 Customer Service 8, 25 5 10 3 7, 33 Amazon Posting (42, 851 -0400, 0. 8) (18, 800 -2027575, 1. 0) (13, 800 -2027575, 1. 0) (78, 800 -3229266, 1. 0) #phone 21
Experiment Setup Corpus: General crawl of the Web(Aug, 2006), around 2 TB with 93 M pages. Entities: Phone (8. 8 M distinctive instances) Email (4. 6 M distinctive instances) System: A cluster of 34 machines 22
Comparing Entity. Rank to the Following Different Approaches Contextual Uncertain Holistic Discriminative Associative Naïve Local Global Combine Without Entity. Rank 23
Online Demo.
Example Query Results 25
Conclusions Formulate Study search the entity search problem and define the characteristics of entity Conceptual Impression Model and concrete Entity. Rank framework for ranking entities An online prototype with real Web corpus 26
Thank You ! Questions?
Reference �Entity. Rank: Searching Entities Directly and Holistically. T. Cheng, X. Yan, and K. C. -C. Chang. In Proceedings of the 33 rd Very Large Data Bases Conference (VLDB 2007), pages 387398, Vienna, Austria, September 2007 � http: //www-forward. cs. uiuc. edu/talks/2007/entityrankvldb 07 -cyc-sep 07. ppt
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