1. Ontology-based fuzzy event extraction agent for Chinese e- news summarization Expert Systems with Applications Volume: 25, Issue: 3, October, 2003, pp. 431-447 Ya-pei Lin ( 林雅珮 )

2. 2 Introduction Ontology collection of –key concepts –interrelationships collectively User and system can communicate with each other by the shared and common understanding of a domain

3. 3 Motivation Prohibited an easy access to the right information. Spend a lot of time manually sifting out useful or relevant information.

4. 4 Goal Summarization is to take from the extracted content Present the most important to the user in a condensed form.

5. 5 Solution Ontology-based Fuzzy Event Extraction (OFEE) agent The OFEE agent consists –Retrieval Agent (RA) –Document Processing Agent (DPA) –Fuzzy Inference Agent (FIA)

6. 6 AAA

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12. 12 Fuzzy Model L–R type fuzzy number m: 指 x 的平均值 α: 指 x 的左散度 β: 指 x 的右散度 α β m

13. 13 AAA

14. 14 Fuzzy Inference Agent Input linguistic layer Input term layer Rule layer Output term layer Output linguistic layer

15. 15 Define of term Part-of-speech (POS) Term Word (TW) Term Frequency (TF)

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17. 17 Input linguistic layer The input vectors are the term set retrieved from Chinese e-news document and domain ontology.

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19. 19 Input term layer(1/9) Three input fuzzy variables –Term POS similarity –Term Word (TW) similarity –Term Frequency (TF) similarity

20. 20 Input term layer(2/9) POS similarity –utilize the length of the path

21. 21 Input term layer (3/9) The path length of the tagging tree is bounded in the interval [0,6]. 123456

22. 22 Input term layer (4/9) TW similarity –compute the number of the same words that different term pairs

23. 23 Input term layer (5/9) The bound of the number of the same word for any Chinese term pair is [0,6]. 123456

24. 24 Input term layer (6/9) TF similarity –for every two Chinese terms located in the retrieved e-news document and e-news domain ontology

25. 25 Input term layer (7/9) The universe of discourse for TF similarity interval is [0,1]. 0.20.30.50.70.81

26. 26 Input term layer (8/9) transferred

27. 27 Input term layer (9/9)

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29. 29 Rule layer (1/2) The rule layer is used to perform precondition matching of fuzzy logic rules.

30. 30 Rule layer (2/2) Hence, each rule node of the rule layer should perform the fuzzy AND operation.

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32. 32 Output term layer (1/2) The output term layer performs the fuzzy OR operation to integrate the fired rules which have the same consequence. The fuzzy variable defined in the output layer is terms relation strength (TRS).

33. 33 Output term layer (1/2) TRS fuzzy set

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35. 35 Output linguistic layer output linguistic layer –Defuzzification process to get the TRS of the Chinese term pair. –Center Of Area (COA) method

36. 36 Event Ontology Filter (1/2)

37. 37 Event Ontology Filter (2/2) EOF is proposed for getting the extracted-event ontology. The EOF utilizes the computing results of FIA and the e-news ontology to extract the e-news event.

38. 38 Summarization Agent (1/2)

39. 39 Summarization Agent (2/2) Document summarization Chinese e-news summary generated Stored into the repository

40. 40 Example (1/4) RA

41. 41 Example (2/4) DPA

42. 42 Example (3/4) FIA & EOF

43. 43 Example (4/4) SA

44. 44 Conclusions In this article, we propose an OFEE agent for Chinese e-news summarization. Summarization is most important to the user in a condensed form. Topic-focused summary is more suitable for full-text searching, browsing

45. Thanks for your listening. Q & A