1. Web Information Retrieval and Extraction Chia-Hui Chang, Associate Professor National Central University, Taiwan chia@csie.ncu.edu.tw

2. Sep. 21, 20042 Course Content Web Information Retrieval Browsing via categories Searching via search engines Query answering Web Information Integration Web page collection Data extraction from semi-structured Web pages Data integration

3. Sep. 21, 20043 Web Categories Yahoo http://www.yahoo.comhttp://www.yahoo.com Fourteen categories and ninety subcategories Categorization by humans Technology Document classification Pros and Cons Overview of the content in the database Browsing without specific targets

4. Sep. 21, 20044 Search Engines Google http://www.google.comhttp://www.google.com Search by keyword matching Business model Technology Web Crawling Indexing for fast search Ranking for good results Pros and Cons Search engines locate the documents not the answers

5. Sep. 21, 20045 Question Answering Askjeeves http://www.ask.comhttp://www.ask.com Input a question or keywords Relevance feedback from users to clarify the targets ExtAns (Molla et al., 2003) Technology Text information extraction Natural Language Processing

6. Sep. 21, 20046 Web Page Collection Metacrawler http://www.metacrawler.com/http://www.metacrawler.com/ Google · Yahoo · Ask Jeeves About · LookSmart · Overture · FindWhat Ebay http://www.ebay.com/http://www.ebay.com/ Information asymmetry between buyers and sellers Technology Program generators WNDL, W4F, XWrap, Robomaker

7. Sep. 21, 20047 Data Extraction from Semi- structured Documents Example Technology Information Extraction Systems WIEN, Softmealy, Stalker, IEPAD, DeLA, OLERA, Roadrunner, EXALG, XWrap, W4F, etc. Data Annotation Wrapper induction is an excellent exercise of machine learning technologies

8. Sep. 21, 20048 Data Integration Technology Template based interface design Microsoft Visual Programming tools

9. Sep. 21, 20049 Available Techniques Artificial Intelligence Search and Logic programming Machine Learning Supervised learning (classification) Unsupervised learning (clustering) Database and Warehousing OLAP and Iceberg queries Data Mining Pattern mining from large data sets Other Disciplines Statistics, neural network, genetic algorithms, etc.

10. Sep. 21, 200410 Classical Tasks Classification Artificial Intelligence, Machine Learning Clustering Pattern recognition, neural network Pattern Mining Association rules, sequential patterns, episodes mining, periodic patterns, frequent continuities, etc.

11. Sep. 21, 200411 Classification Methods Supervised Learning (Concept Learning) General-to-specific ording Decision tree learning Bayesian learning Instance-based learning Sequential covering algorithms Artificial neural networks Genetic algorithms Reference: Mitchell, 1997

12. Sep. 21, 200412 Clustering Algorithms Unsupervised learning (comparative analysis) Partition Methods Hierarchical Methods Model-based Clustering Methods Density-based Methods Grid-based Methods Reference: Han and Kamber (Chapter 8)

13. Sep. 21, 200413 Pattern Mining Various kinds of patterns Association Rules Closed itemsets, maximal itemsets, non-redundant rules, etc. Sequential patterns Episodes mining Periodic patterns Frequent continuities

14. Sep. 21, 200414 Applications Relational Data E.g. Northern Group Retail (Business Intelligence)Northern Group Retail Banking, Insurance, Health, others Web Information Retrieval and Extraction Bioinformatics Multimedia Mining Spatial Data Mining Time-series Data Mining

15. Sep. 21, 200415 Techniques from Information Retrieval (IR) Text Operations Lexical analysis of the text Elimination of stop words Index term selection Indexing and Searching Inverted files Suffix trees and suffix arrays Signature files Ranking Models Query Operations Relevance feedback Query expansion

16. Sep. 21, 200416 Course Schedule Techniques from Information Retrieval Text Operations Indexing and Searching Ranking Models Query Operations Text Information Extraction for Query answering AutoSlog, SRV, Rapier, etc. Data extraction from semi-structured Web pages WIEN, Softmealy, Stalker, IEPAD, DeLA, Roadrunner, EXALG, OLERA, etc. Web page collection XWrap, W4F, Robomaker, etc.

17. Sep. 21, 200417 Grading Two projects (by groups): 50% Chosen from the topics covered in the course Presentation and reports Paper reading (by yourself): 20% Presentation Information Integration Projects: 30% Chosen freely Presentation and reports

18. Sep. 21, 200418 References Baeza-Yates, R. and Ribeiro-Neto, B. 1999. Modern Information Retrieval, Addison Wesley Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Mitchell, T. M. 1997. Machine Learning, McGRAW- HILL. Molla, D., Schwitter, R., Rinaldi, F., Dowdall, J. and Hess, M. 2003. ExtrAns: Extracting Answers from Technical Texts. IEEE Intelligent Systems, July/August 2003, 12-17.