1. FACT: A Learning Based Web Query Processing System Hongjun Lu, Yanlei Diao Hong Kong U. of Science & Technology Songting Chen, Zengping Tian Fudan University

2. Demonstration, SIGMOD 2000 2 Outline  Introduction  Learning Based Web Query Processing  FACT: A Prototype System  Preliminary System Evaluation  Conclusions

3. Demonstration, SIGMOD 2000 3 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 Kong+hotel

4. Demonstration, SIGMOD 2000 4 Hotel 2 Hotel 1 3 done forward Look at the Number!

5. Demonstration, SIGMOD 2000 5 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?

6. Demonstration, SIGMOD 2000 6 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, …

7. Demonstration, SIGMOD 2000 7 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 :-(

8. Demonstration, SIGMOD 2000 8 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

9. Demonstration, SIGMOD 2000 9 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.

10. Demonstration, SIGMOD 2000 10 Outline  Introduction  Learning Based Web Query Processing  FACT: A Prototype System  Preliminary System Evaluation  Conclusions

11. Demonstration, SIGMOD 2000 11 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!

12. Demonstration, SIGMOD 2000 12 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

13. Demonstration, SIGMOD 2000 13 Hotel 2 Hotel 1 3 done forward User browses it!

14. Demonstration, SIGMOD 2000 14 Back User clicks here!

15. Demonstration, SIGMOD 2000 15 Room information User marks it!

16. Demonstration, SIGMOD 2000 16 back Fact starts here!

17. Demonstration, SIGMOD 2000 17 roomrates Fact chooses it!

18. Demonstration, SIGMOD 2000 18 xxx Fact finds it!

19. Demonstration, SIGMOD 2000 19 Outline  Introduction  Learning Based Web Query Processing  FACT: A Prototype System  Preliminary System Evaluation  Conclusions

20. Demonstration, SIGMOD 2000 20 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

21. Demonstration, SIGMOD 2000 21 Reference Architecture Session Controller Locator Search Engine Web User Interface Knowledge Base Learner Query Analyzer Crawler & Parser User

22. Demonstration, SIGMOD 2000 22 A Query Session Session Controller Training Strategy Segment Graph Result Buffer Knowledge Base User Actions Query results Checking URLs Locating Process Locator Query Result Presenter Learning Process Learner Browser Scripts

23. Demonstration, SIGMOD 2000 23 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 n 0 sites, user browses and system learns  Next n - n 0 site, system makes decision. For incorrect ones, user browses and system re-learns  Next N-n sites: system processes

24. Demonstration, SIGMOD 2000 24 Outline  Introduction  Learning Based Web Query Processing  FACT: A Prototype System  Preliminary System Evaluation  Conclusions

25. Demonstration, SIGMOD 2000 25 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

26. Demonstration, SIGMOD 2000 26

27. Demonstration, SIGMOD 2000 27 System Evaluation - Effectiveness  Given a set of keywords, the system makes N decisions N =N1 + N2 + N3 + N4 Precision = N1 / (N1+N3), Recall= N1 / # relevant sites, Correctness = (N1+N2) / N.

28. Demonstration, SIGMOD 2000 28 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.

29. Demonstration, SIGMOD 2000 29 Basic Performance Q 11 : Hong Hong Hotel Room Rate Q 12 : Hong Kong Hotel Sequential training

30. Demonstration, SIGMOD 2000 30 Query Q 12 Effects of training Strategies

31. Demonstration, SIGMOD 2000 31 Improved Performance Interleaved training

32. Demonstration, SIGMOD 2000 32 Outline  Introduction  Learning Based Web Query Processing  FACT: A Prototype System  Preliminary System Evaluation  Conclusions

33. Demonstration, SIGMOD 2000 33 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

34. Demonstration, SIGMOD 2000 34 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 $$$ ???