基于本体的阿育吠陀医学语义搜索系统框架

Gayathri M, Dr. Jagadeesk Kannan R.
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引用次数: 0

摘要

印度以其传统的医学体系而闻名,如阿育吠陀、瑜伽、乌纳尼、悉达和顺势疗法。阿育吠陀在治疗疾病方面发挥着重要作用,没有任何副作用。药用植物或草药被认为是满足人们保健需要的主要资源。有关这些医学知识的信息必须保存并数字化。以非结构化文本数据的形式出现了大量关于阿育吠陀研究的出版物和文章。本文采用文本挖掘的方法,为处理大量非结构化数据提供了解决方案。随着基于文本的数据呈指数级增长,导航所需的相关信息是一项具有挑战性的任务。对文档内容的语义理解是保证内容检索质量的重要要求。然而,目前的方法发现文本分类会带来分类精度的变化,在分类过程中可能无法理解数据。因此,需要一个高效的模型来搜索、分类和检索最相关的数据。本研究的主要目标是通过应用基于本体的文本挖掘方法,开发一个有效的框架和算法来搜索和检索最相关的事实。分析和回顾了语义网检索、基于本体的方法和构建框架的各种分类技术的研究现状。文本挖掘的重点是理解内容的语义,通过领域本体——药用植物本体构建来实现。通过语义网和本体来解决查找给定查询的语义相关内容的难题,从而丰富了web上的数据进行结构化表示,从而为知识表示提供了强语义。利用具有语义知识表示的药用植物本体实现了信息提取的方法,提出了一种基于本体的概念提取和分类算法,通过在药用植物本体中映射术语及其相关术语来对每个术语进行语义描述。web本体语言(web Ontology language, OWL)是一种用于知识表示的网络语言,被认为是一种更丰富的语义描述语言,用于描述网络上的非结构化和半结构化内容,从而提取准确和相关的数据,并提供强大的语义搜索。为了评估所提出方法的性能,从在线资源和数字图书馆收集了不太相关和最相关的文档。对不同的分类技术进行了比较研究。实验结果表明,该方法是有效的。为了进一步证明该模型的有效性,通过给出不同的查询进行了实验,并将结果与其他现有方法进行了比较。结果表明,该模型提高了检索结果的查全率和查全率。关键词:传统医学,阿育吠陀,本体,语义网,Web本体语言
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework for Ontology Based Semantic Search System in Ayurvedic Medicine
India is known for its traditional medicinal system such as Ayurveda, Yoga, Unani, Siddha and Homeopathy. Ayurveda plays a significant role in curing the diseases without any side effects. Medicinal plants or herbs are considered as a major resource in meeting the need of people health care. Information about this medicinal knowledge must be preserved and digitized. There have been a massive number of publications and large number of articles on ayurvedic research in the form of unstructured textual data. Text mining approach is used to provide the solution to handle such voluminous of unstructured data. With the exponential growth of text based data, navigating the relevant information needed is the challenging task. Semantic understanding of document content forms the vital requirement for ensuring the quality of content retrieval. However, the current approaches are finding variation in textual classification in bringing the classification accuracy which may fail to understand the data during classification. Hence, an efficient model is required to search, classify and retrieve the most relevant data. The main objective of this research is to develop an effective and efficient framework and algorithm to search and retrieve the most relevant facts by including the application of ontology-based text mining approach. The current status of research is analyzed and reviewed in the area of semantic web retrieval, ontology-based approaches and various classification technique for building the framework. Text mining with the special emphasis on understanding the semantic meaning of content is achieved by using domain ontology called medicinal plant ontology construction. The challenges in finding the semantically related content for the given query are achieved through semantic web and ontology which enriched the data on web for structured representation thereby providing the strong semantics in knowledge representation. The methodology of information extraction is implemented by using medicinal plant ontology with semantic knowledge representation, an algorithm called OCEC (Ontology based Concept Extraction and Classification) was developed where each term is described semantically by mapping the terms and its related terms in the medicinal plant ontology. The web language called Web Ontology Language (OWL) is used for knowledge representation and is considered as richer semantic description language for describing unstructured and semi-structured content on the web thereby extracting the exact and relevant data and to offer a strong semantic search. To evaluate the performance of the proposed method, less relevant and most relevant documents were collected from online sources and digital libraries. Comparative study has been performed with various classification techniques. The experimental results show that the proposed method out performed. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model improves precision and recall results. Keyword : Traditional Medicine, Ayurveda, Ontology, Semantic Web, Web Ontology Language
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