A Fuzzy Ontology and Its Application to News Summarization

A Fuzzy Ontology and Its Application to News Summarization

Abstract:

In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.

Existing System:

The FIDS can integrate the information from different articles by conducting automatic content-based classification and information item extraction. Provide a framework, known as S-CREAM, that allows for creation of metadata and is trainable for a specific domain. Ont-O-Mat is the reference implementation of the S-CREAM framework. It provides a plug in interface for extensions for further advancements, e.g., collaborative metadata creation or integrated ontology editing and evolution.

 

 

Disadvantage:

The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier.

 

Proposed System:

In this paper, we present a fuzzy ontology and apply it to news summarization. The fuzzy ontology is an extension of the domain ontology that is more suitable to describe the domain knowledge for solving the uncertainty reasoning problems. In addition, a news agent based on the fuzzy ontology is also developed for news summarization.