FACT: A Learning Based Web Query Processing System

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FACT A Learning Based Web Query Processing System

• Hongjun Lu, Yanlei Diao
• Hong Kong U. of Science Technology
• Songting Chen, Zengping Tian
• Fudan University

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Outline

• Introduction
• Learning Based Web Query Processing
• FACT A Prototype System
• Preliminary System Evaluation
• Conclusions

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How Do We Query the Web?

• Use a search engine
• Form query key words
• An example Find room rates of hotels in Hong
  Kong
• used search engine www.yahoo.com
• keywords Hong Konghotel

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forward
Hotel 1
3
Hotel 2
Look at the Number!
done
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Query the Web -- Current Situation

• Search engines return a long list of URLs. User
  is required to browse the web pages to find the
  information.
• The information required is often not on the
  returned page -- navigation through hyperlinks is
  often required (those links may or may not that
  obvious).
• The target information is in different forms
  (paragraphs, lists, tables )
• A lot of web pages to be browsed

Are we happy with this?
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Efforts to Improve the Situation

• Search engines
• better index, improve precision/recall,
  metasearch engines, better presentation of
  results, .
• IR techniques to Web
• document clustering/indexing, better model,
  similarity functions, documents ranking, ...
• Intelligent agent
• user profiling, hyperlink recommendation, ...
• Database approach
• wrappers, query languages,

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Our Dream

• Querying the Web as easy as querying a relational
  database
• SQL query returns a table of hotel prices
• SELECT room rates
• FROM web.hotel
• WHERE city hong kong
• May remain a dream for a while -(

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A Practical goal

• Use keywords to express query requirements
• simple, no need to know schema of data
• inaccurate
• Relieve users from tedious browsing as much as
  possible
• Not URLs, not Web sites, even not Web pages
• Present query results to users as accurate and
  concise as possible
• Tables, lists, paragraphs, containing user
  required information

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Query Results -- Queried Segments

• Return query results as accurate and concise as
  possible.
• Basic idea
• Breaking a Web page into segments a row in a
  table, a table, an item in a list, a list, a
  paragraph,
• returning only queried segments to users
• queried segments segments that contain the
  information the user is interested in .

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Outline

• Introduction
• Learning Based Web Query Processing
• FACT A Prototype System
• Preliminary System Evaluation
• Conclusions

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Learning Based Query Processing

• The fundamental difficulties in Web query
  processing
• Web is a huge, ever growing, heterogeneous,
  semi-structured data source
• Most users of Web are naïve users issuing ad hoc
  queries
• Learn the knowledge for query processing from the
  User!

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A Learning Based Technique

• Learn from the user when he browses from the
  first few URLs
• to navigate through the web pages
• to identify the required information in a web
  page
• Process the rest URLs automatically and retrieve
  queried segments

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forward
Hotel 1
3
Hotel 2
User browses it!
done
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Back
User clicks here!
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Room information
User marks it!
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back
Fact starts here!
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roomrates
Fact chooses it!
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xxx
Fact finds it!
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Outline

• Introduction
• Learning Based Web Query Processing
• FACT A Prototype System
• Preliminary System Evaluation
• Conclusions

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A Query Processing System

• A learning based query processing system
• User Interface accepts user queries, presents
  query results, a browser capable of capturing
  user actions
• Query Analyzer analyzes and transforms user
  queries
• Session Controller coordinates learning and
  locating
• Learner generates knowledge from captured user
  actions
• Locator applies knowledge and locates query
  results
• Crawler Parser retrieves pages and parses to
  trees
• Knowledge Base stores learned knowledge

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Reference Architecture
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A Query Session
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Training Strategies

• Sequential
• First n sites user browses and system learns
• Next N-n sites system processes
• Random
• Randomly choose n sites user browses and system
  learns
• the system processes the rest
• Interleaved
• First n0 sites, user browses and system learns
• Next n - n0 site, system makes decision. For
  incorrect ones, user browses and system re-learns
  
• Next N-n sites system processes

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Outline

• Introduction
• Learning Based Web Query Processing
• FACT A Prototype System
• Preliminary System Evaluation
• Conclusions

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System Evaluation

• Functionality
• Performance
• precision, recall, correctness
• efficiency in a site, how many pages the system
  visits to find a result
• training efficiency how many training samples
  are needed
• User interface

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System Evaluation - Effectiveness

• Given a set of keywords, the system makes N
  decisions
• N N1 N2 N3 N4
• Precision N1 / (N1N3) ,
• Recall N1 / relevant sites ,
• Correctness (N1N2) / N .

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System Evaluation - Efficiency

• How efficiently the system finds a queried
  segment in a site?
• Level of a Queried Segment the length of the
  shortest path to find it
• Absolute Path length Crawled pages,
• Relative Path Length Crawled pages / Level of
  the Queried Segment .

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Basic Performance

Q11 Hong Hong Hotel Room Rate Q12 Hong Kong
Hotel
Sequential training
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Query Q12 Effects of training Strategies
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Improved Performance
Interleaved training
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Outline

• Introduction
• Learning Based Web Query Processing
• FACT A Prototype System
• Preliminary System Evaluation
• Conclusions

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Conclusions

• Proposed and implemented learning based Web query
  processing with the following features
• Returning succinct results segments of pages
• No a prior knowledge or preprocessing, suited for
  ad hoc queries
• exploiting page formatting and linkage
  information simultaneously.
• The preliminary results are promising

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Future Work

• Better knowledge
• key factor that affects system performance
• Dynamic web pages ?
• Integrating results from another project
• System evaluation
• Prototype ? product ? dot com company
• ???