基于文本挖掘的结核本体生成与浓缩

Desi Ramayanti, Vina Ayumi, Handrie Noprisson, Anita Ratnasari, I. Handriani, Marissa Utami, Erwin Dwika Putra
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引用次数: 8

摘要

本体是一种用于语义网的知识表示模型。在本体中进行领域丰富需要一种快速有效的方法,这可以通过使用文本挖掘方法来实现。本研究采用文本挖掘的方法来丰富流行病学领域,特别是结核病领域的本体。本研究利用现有的本体论,即流行病学本体论(Epidemiology ontology, EPO)和各种关于结核病的科学文献数据。从有关结核病(肺结核)的科学文献中获得的数据将用于丰富流行病学本体(EPO)。在这项研究中,浓缩是半自动完成的,术语和概念的提取是自动从自然语言语料库中完成的,验证是由流行病学专家参与手动完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tuberculosis Ontology Generation and Enrichment Based Text Mining
Ontology is a knowledge representation model that used in the semantic web. To perform domain enrichment in the ontology needed a fast and efficient method, this can be achieved by using text mining approaches. This research was exacting text mining approach to enrich ontology in epidemiology domain, especially in tuberculosis. This study utilizes existing ontology, namely Epidemiology Ontology (EPO) and data from a variety of scientific documents about tuberculosis. The data obtained from scientific documents regarding tuberculosis (pulmonary TB) will be used to enrich Epidemiology Ontology (EPO). In this study enrichment is done semi-automatically, term and concept extraction is done automatically from a natural language corpus and validation is done manually by involving epidemiology experts.
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