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![Page 1: Ontology-based fuzzy event extraction agent for Chinese e- news summarization Expert Systems with Applications Volume: 25, Issue: 3, October, 2003, pp.](https://reader035.fdocuments.net/reader035/viewer/2022081520/5697c0021a28abf838cc2ca7/html5/thumbnails/1.jpg)
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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• AAA
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• AAA
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• AAA
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• AAA
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• AAA
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• AAA
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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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• AAA
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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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• 111
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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
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Thanks for your listening.
Q & A