Opportunities for AI in Intelligent Web-based Technology-Supported Learning

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Talk I gave at WISM’09-AICI'09 (2009 International Conference on Web Information Systems and Mining and the 2009 International Conference on Artificial Intelligence and Computational Intelligence) where I describe research opportunities offered by today's Web 2.0 and Semantic Web.

Technologie

Opportunities for AI in Intelligent Web-based Technology-Supported Learning Carsten Ullrich Jian Wang Ruimin Shen Frank Quosdorf

Today’s Web: enormous potential for AI in technology supported learning.

Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited

Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited This talk: Trends

Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited This talk: Trends Example

Mash-ups Yahoo! Pipes http://www.flickr.com/photos/seeminglee/1950911618/

Personal Learning Environments http://role-project.eu

Architecture of Assembly makes it easier to build complex services

Architecture of Assembly adds to Web of documents a layer of reusable services

Opportunities: -access to huge amounts of data useful for learning process; -analyze user generated content, e.g., to make estimations about their knowledge/competencies; -finding/combining services and combining to train specific skills.

Trend: User-Centered Web focuses on the individual using these documents and services

OpenID: decentralized single-sign-on mechanism OAUTH: information exchange between services OpenSocial: APIs for common functionality for social applications

Information about user: Attention Profiling Markup Language (APML) Location data: Google Gears, Firefox

Amazing amounts of data about user available Opportunity: use this data for personalization

Access to data no longer problem Problematic: Find specific relevant piece of information

Access to data no longer problem Problematic: Find specific relevant piece of information Combine data

Linked Open Data Initiative Datasets include Wikipedia, DBLP, RKB Explorer, CIA World Fact Book, OpenCyc, …

Entity Extraction ,[object Object],This is what it looks like to the machine!

Entity Extraction ,[object Object],OpenCalais ,[object Object],[object Object]

Opportunities: - Semantic Content and Semantics for free enable amazing possibilities: improved quality of service, better categorization, recommendation, automatic linking with other resources, etc.

- research workbench - bibliography manager - research network - support while writing research papers

[object Object],[object Object],[object Object]

Semantifying ,[object Object],OpenCalais ,[object Object],[object Object],DBPedia (others: Yago, Freebase, UMBEL) ,[object Object],[object Object]

Related Topics: Web_2.0 in DBPedia ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Most services: English only Services can become unavailable

Lesson: To advance science, make research results available to other parties, as a Web service or open source.

Ground breaking services and data have now become basic commodity By building upon them, research in Technology-Enhanced Learning can tackle more meaningful problems

谢谢！ [email_address] http://www.carstenullrich.net http:// www.slideshare.net/ullrich

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<<< Slides can be found at http://www.slideshare.net/denshe/intelligent-crawling-shestakovwiiat13 >>> ------------------- Web crawling, a process of collecting web pages in an automated manner, is the primary and ubiquitous operation used by a large number of web systems and agents starting from a simple program for website backup to a major web search engine. Due to an astronomical amount of data already published on the Web and ongoing exponential growth of web content, any party that want to take advantage of massive-scale web data faces a high barrier to entry. We start with background on web crawling and the structure of the Web. We then discuss different crawling strategies and describe adaptive web crawling techniques leading to better overall crawl performance. We finally overview some of the challenges in web crawling by presenting such topics as collaborative web crawling, crawling the deep Web and crawling multimedia content. Our goals are to introduce the intelligent systems community to the challenges in web crawling research, present intelligent web crawling approaches, and engage researchers and practitioners for open issues and research problems. Our presentation could be of interest to web intelligence and intelligent agent technology communities as it particularly focuses on the usage of intelligent/adaptive techniques in the web crawling domain. -------------------

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Opportunities for AI in Intelligent Web-based Technology-Supported Learning

1. Opportunities for AI in Intelligent Web-based Technology-Supported Learning Carsten Ullrich Jian Wang Ruimin Shen Frank Quosdorf

2. Today’s Web: enormous potential for AI in technology supported learning.

3. Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited

4. Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited This talk: Trends

5. Today’s Web: enormous potential for AI in technology supported learning. But potential is still under-exploited This talk: Trends Example

6. Trends: Architecture of Assembly

7. APIs

10. Your own system

11. RSS

12. New Articles

13. New Data

14. Mash-ups Yahoo! Pipes http://www.flickr.com/photos/seeminglee/1950911618/

15. Widgets

16. Personal Learning Environments http://role-project.eu

17.

18. Architecture of Assembly makes it easier to build complex services

19. Architecture of Assembly adds to Web of documents a layer of reusable services

20. Opportunities: -access to huge amounts of data useful for learning process; -analyze user generated content, e.g., to make estimations about their knowledge/competencies; -finding/combining services and combining to train specific skills.

21. Trend: User-Centered Web

22. Trend: User-Centered Web focuses on the individual using these documents and services

23. Network of documents Network of people

24. Live Streams Facebook Friendfeed

25. Today: Silos

26. OpenID: decentralized single-sign-on mechanism OAUTH: information exchange between services OpenSocial: APIs for common functionality for social applications

27. Information about user: Attention Profiling Markup Language (APML) Location data: Google Gears, Firefox

28. Amazing amounts of data about user available Opportunity: use this data for personalization

29. Trend: Semantics

30. Access to data no longer problem

31. Access to data no longer problem Problematic: Find specific relevant piece of information

32. Access to data no longer problem Problematic: Find specific relevant piece of information Combine data

33. Semantic Web

34. Linked Open Data Initiative Datasets include Wikipedia, DBLP, RKB Explorer, CIA World Fact Book, OpenCyc, …

35.

36.

37.

38. How to Get Semantics?

39.

40.

41.

42.

43. Opportunities: - Semantic Content and Semantics for free enable amazing possibilities: improved quality of service, better categorization, recommendation, automatic linking with other resources, etc.

44. Example: Totuba Toolkit

45. - research workbench - bibliography manager - research network - support while writing research papers

46.

47.

48.

49. Problems

50. Most services: English only Services can become unavailable

51. Lesson: To advance science, make research results available to other parties, as a Web service or open source.

52. Conclusion

53. Ground breaking services and data have now become basic commodity By building upon them, research in Technology-Enhanced Learning can tackle more meaningful problems

54. 谢谢！ [email_address] http://www.carstenullrich.net http:// www.slideshare.net/ullrich

Opportunities for AI in Intelligent Web-based Technology-Supported Learning

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