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 )

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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

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Motivation

• Prohibited an easy access to the right information.

• Spend a lot of time manually sifting out useful or relevant information.

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Goal

• Summarization is to take from the extracted content

• Present the most important to the user in a condensed form.

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Solution

• Ontology-based Fuzzy Event Extraction (OFEE) agent

• The OFEE agent consists – Retrieval Agent (RA)– Document Processing Agent (DPA) – Fuzzy Inference Agent (FIA)

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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

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Fuzzy Inference Agent

• Input linguistic layer• Input term layer• Rule layer• Output term layer• Output linguistic layer

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Define of term

• Part-of-speech (POS)• Term Word (TW)• Term Frequency (TF)

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Input linguistic layer

• The input vectors are the term set retrieved from Chinese e-news document and domain ontology.

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Input term layer(1/9)

• Three input fuzzy variables – Term POS similarity– Term Word (TW) similarity – Term Frequency (TF) similarity

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Input term layer(2/9)

• POS similarity – utilize the length of the path

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Input term layer (3/9)

• The path length of the tagging tree is bounded in the interval [0,6].

1 2 3 4 5 6

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Input term layer (4/9)

• TW similarity– compute the number of the same

words that different term pairs

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Input term layer (5/9)

• The bound of the number of the same word for any Chinese term pair is [0,6].

1 2 3 4 5 6

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Input term layer (6/9)

• TF similarity – for every two Chinese terms located

in the retrieved e-news document and e-news domain ontology

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Input term layer (7/9)

• The universe of discourse for TF similarity interval is [0,1].

0.2 0.3 0.5 0.7 0.8 1

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Input term layer (8/9)

transferred

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Input term layer (9/9)

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Rule layer (1/2)

• The rule layer is used to perform precondition matching of fuzzy logic rules.

•

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Rule layer (2/2)

• Hence, each rule node of the rule layer should perform the fuzzy AND operation.

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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).

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Output term layer (1/2)

• TRS fuzzy set

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Output linguistic layer

• output linguistic layer – Defuzzification process to get the TRS of t

he Chinese term pair.– Center Of Area (COA) method

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Event Ontology Filter (1/2)

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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.

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Summarization Agent (1/2)

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Summarization Agent (2/2)

• Document summarization• Chinese e-news summary

generated • Stored into the repository

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Example (1/4)

RA

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Example (2/4)

DPA

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Example (3/4)

FIA & EOF

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Example (4/4)

SA

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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

Thanks for your listening.

Q & A