A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

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 changingits contents into machine understandable form. Therefore, semantic level information is one of thecornerstones of the Semantic Web. The process of adding semantic metadata to web resources is calledSemantic 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 systemsmust be performed on, the problem of automating annotation process is one of the significant challenges inthis domain. To overcome this problem, different machine learning approaches such as supervisedlearning, unsupervised learning and more recent ones like, semi-supervised learning and active learninghave been utilized. In this paper we present an inclusive layered classification of Semantic Annotationchallenges and discuss the most important issues in this field. Also, we review and analyze machinelearning applications for solving semantic annotation problems. For this goal, the article tries to closelystudy and categorize related researches for better understanding and to reach a framework that can mapmachine learning techniques into the Semantic Annotation challenges and requirements.