deep web crawling and mining

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