Upload
nemo
View
32
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Deep Web Crawling and Mining. Presented by: Group 17 AIA 8803 Course Feb 28, 2008. What ’ s the Problem?. Large Amount of Deep Web Content Refers to World Wide Web content that is not part of the surface Web indexed by search engines (Bergman, 2001) - PowerPoint PPT Presentation
Citation preview
Deep Web Crawling and Mining
Presented by: Group 17
AIA 8803 CourseFeb 28, 2008
What’s the Problem? Large Amount of Deep Web Content
Refers to World Wide Web content that is not part of the surface Web indexed by search engines (Bergman, 2001)
In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents
Characteristics of Deep Web Data: Mostly generated by backend database Intrinsic – behind database scheme
Our solution Deep web crawling
Iterative querying Deep web mining
Attribute labeling Advanced search
Database construction Object-level search Comparison
Deep Web Crawling Why it’s difficult in dynamic web space?
Hidden Web, Deep Web Different from traditional web crawler where a
hyperlink graph is traversed with BFS or WFS to crawl web pages
Seed-based crawler Seed Crawl New Seed Crawl …
An Crawler Example Initial seed: car New seeds: Lincoln, Deluxe, TracRac, Truc
k, SUV
Deep Web Mining What we have:
Large amount of web pages gathered from the crawler
Machine Learning /
Data Mining
techniques
What we need: A structured database for web application
Deep Web Mining
Problem Different web sites may have different layouts
Deep Web Mining Conditional Random Fields (CRFs)
An undirected graphic model X (Gray nodes): observations
Features extracted from the crawled web pages Y (White nodes): hidden states
Labels Product name, price, customer rating, etc..
CRF models the conditional probability p(y|x) Key advantage
Rich, correlated feature sets
Web database from mining Data fusion will be necessary where
multiple copies of data exist across sites
What We Have• Web object extraction and mining• Structured databases of web objects
Next Step• improve the state-of-the-arts Web search• make some money
Building Advanced Web Search Application
1. object-level web search combine different features or attributes of an identical Web object in different Web sites to respond to a user query
DBLP (manual but high-precise) Citeseer (auto but less-precise)
Challenge is on how to build an precise and automatic object-level search platform DBLP?
2. comparison Web searchcompare attributes (e.g. price, performance, etc) of Web objects across different sites or sources
Building a LAMP Server "LAMP" system: Linux, Apache, MySQL an
d PHP.
1. low acquisition cost
2. ubiquity of its components
Fancy restaurant (dynamic web server) Apache: chef. PHP: waiter. MySQL: stockroom of ingredients When a patron (or Web site visitor) comes to your restaurant, he or sh
e sits down and orders a meal with specific requirements. The waiter (PHP) takes those specific requirements back to the kitche
n and passes them off to the chef (Apache). The chef then goes to the stockroom (MySQL) to retrieve the ingredie
nts (or data) to prepare the meal and presents the final dish to the patron, exactly the way he or she ordered the meal.
Thank you.
Q&A