The study of Cross-Languages Diabetes Information Question Answering System Based on Domain Ontology
碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 101 === In recent years, diabetic patients are increasing quickly. And with the rapid development of network technology, users can query information through the browser and search engine. However, there are two main barriers for search engines. The first one is languag...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/77431143439948538767 |
| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 101 === In recent years, diabetic patients are increasing quickly. And with the rapid development of network technology, users can query information through the browser and search engine. However, there are two main barriers for search engines. The first one is language barrier and the second one is search barrier. In search barrier, the query results have many species and the contents are too complex that users must look for the contents of web pages in order to confirm the search results are correct and the results are users needed which waste much time and work. Therefore, a technology of question answering system has been more attention. A question answering system provides relevant information to users for correct answers questions. On the other hand, users use search engines exist the other problem that the access information is limited to the same keywords and query languages. This is a language barrier which leads to information reduction. The main purpose of this study combined domain-specific of the ontology for the question answering system with cross-language information retrieval technology to develop a diabetes information cross- language question answering system. The system offers users to inputting query questions and to understand what information users want through the natural language processing based on multi-language ontology to provide appropriate and correct answer. The primary experiments prove that our system has more than 70% accuracy for on line language query and answer for diabetic information.
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