Outline

1. Genetic Fuzzy Neural Agents Using Type-2 Fuzzy Reasoning for Intelligent Web Information Search Task

2. Outline • Problems of current search services • Search agent for a Web Information Search Task • Homepage-Finder - a case study of WIST • Structure Rules • Type-1 Fuzzy Reasoning • Type-2 Fuzzy Reasoning • Simulation Results • Conclusions and future works

3. Problems • Finding the desired information on the World Wide Web is not an easy task because the information available on the WWW is inherently unordered, distributed, and heterogeneous [9]. • As a result, "the ability to search and retrieve information from the Web efficiently and effectively is a key technology for realizing its full potential." [10].

4. Problems –How to make a search interface more expressive? • Drawbacks of current keyword-based search interfaces • Fuzzy search requests • Partial related Web pages • Structure characteristics; content characteristics

5. Problems – How to retrieve information more effectively? • Current search services have poor accuracy • Low recall (fraction of desired documents that are retrieved) • Low precision (fraction of retrieved documents that are desired) • Current search services return results in a non-desired order

6. Web Information Search Task (1) • Observation: Many search requests have different content characteristics but share similar structure characteristics. • SR1: a user wishes to find "all personal homepages of researchers whose research interests are in the field of artificial intelligence". • SR2: "I want to find the information of all faculties who are members of CWI project"

7. Web Information Search Task (2) • Methodology: • Structure Characteristics -> Structure rules • IF part ( a Boolean formula for structure chars) • THEN part • Type-0 Fuzzy function (crisp) • Type-1 Fuzzy function • Type-2 Fuzzy function • FNN to infer the possibility

8. Web Information Search Task (3)

9. Web Information Search Task (4) • Goal: • After learning, a search service built on WIST model will be more expressive and retrieve information more effectively/accurately. • The agent is "intelligent" because it can • learn to get better parameters of FNN, • learn to get better structure of FNN (semi-automatically currently), and • learn to define and update structure characteristics (manually currently) by adding/modifying structure rules.

10. Case Study: Homepage-Finder • It is common for us to look for the information of faculties and/or students with some common characteristics such as similar research interests, or in a same group, etc. • navigate the university’s or department’s website link by link to find their homepages manually (time consuming) • search them by submitting keywords to search engines like Google to retrieve their homepages (difficult to get accurate list) • Homepage-Finder

11. Homepage-Finder : Definition • Homepage-Finder is an intelligent software agent which uses CI technologies including FL, NN, and GA to define and implement a specific WIST to automatically retrieve and rank relevant researchers’ homepages based on • the possibility whether a Web page is a personal homepage and • the relevance with keywords (by Google’s PageRank), given some root URLs, some keywords and some "structure rules".

12. Homepage-Finder: How it works How to retrieve desired Web pages? • Collects the keyword-matched web pages in the web site referred by a Root URL as input data • Scores every web page based on the “structure rules” defined on its URL, Title, and Text. So a web page gets 3 scores called “URL-score”, “Title-score”, and “Text-score”, respectively, as 3 input attributes. • Infers the possibility whether the web page is a personal home page by TSK-based type-1/type-2 fuzzy reasoning.

13. Homepage-Finder: Structure Rules • Structure rules will function as fuzzifier in FLS • 1 sample of Structure Rules in Homepage-Finder: IF a Web page’s Title string includes “homepage” or “home page”, • THEN it is a personal homepage. (Type-0) • THEN its Titlescore is title1. (Type-1) • THEN its Titlescore is in [title1-q2, title1+q2]. (Interval Type-2) • Rule Uncertainty Problem

14. Homepage-Finder: Type-1 Fuzzy Reasoning • Two linguistic variables “HIGH” and “LOW” are defined on the 3 input attributes: • "HIGH" means "how much possibility a Web page is a personal homepage", • "LOW" means "how much possibility a Web page is NOT a personal homepage". • Homepage-Finder has 8 fuzzy rules and does type-1 fuzzy reasoning by original product-sum operation.

16. Homepage-Finder: Interval Type-2 Fuzzy Reasoning • URLscore is in [urlvalue1, urlvalue2] • Titlescore is in [titlevalue1, titlevalue2] • Textscore is in [textvalue1, textvalue2] • (urlvalue1, titlevalue1, textvalue1),(urlvalue1, titlevalue1, textvalue2), • (urlvalue1, titlevalue2, textvalue1),(urlvalue1, titlevalue2, textvalue2), • (urlvalue2, titlevalue1, textvalue1),(urlvalue2, titlevalue1, textvalue2), • (urlvalue2, titlevalue2, textvalue1),(urlvalue2, titlevalue2, textvalue2) • If avgtotalscore is in • [0,0.45], Totalscore = mintotalscore, • (0.45, 0.65], Totalscore = avgtotalscore, • (0.65,1], Totalscore = maxtotalscore. • In this way, the computation time is just increased by a constant factor

17. User Interface of Homepage-FinderGA

18. Performance Evaluations and Conclusion TABLE V Simulation results including the all 4 departments - cs.gsu.edu - cs.caltech.edu - csee.usf.edu - cs.berkeley.edu

19. Future Directions • Try more complicate type-2 fuzzy reasoning while keep the computation time lengthened and thus keep the search speed from slowed. • Homepage-Finder adopts the exact keyword matching. In the future, we want to replace it with the fuzzy concept matching [15] method to search more effectively.

20. Thank you! • Questions?