基于机器学习的语义标注需求分析框架
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
摘要：
语义Web是当前Web的扩展，其中信息被赋予了定义明确的含义。语义网的前景是通过将现有的网络内容转换成机器可理解的形式来提高网络的质量和智能性。因此，语义级信息是语义Web的基石之一。将语义元数据添加到Web资源的过程称为语义注释。语义标注存在着许多障碍，如多语言性、可扩展性以及与不同网页内容的多样性和不一致性有关的问题。由于语义标注系统所涉及的领域非常广泛，而且其所处的环境也是动态的，因此标注过程的自动化是该领域面临的一个重要挑战。为了克服这个问题，已经利用了不同的机器学习方法，诸如监督学习、无监督学习和更近期的方法，如半监督学习和主动学习。在本文中，我们提出了一个包容性的分层分类的语义标注的挑战，并讨论了在这一领域的最重要的问题。此外，我们回顾和分析机器学习应用解决语义标注问题。为此，本文试图对相关研究进行深入研究和分类，以更好地理解语义标注，并得出一个框架，将机器学习技术映射到语义标注的挑战和需求中。
英文摘要：
The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
