10,778 research outputs found
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 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
Semantic Annotation and Information Visualization for Blogposts with refer
The growing amount of documents in archives and blogs results in an increasing challenge for curators and authors to tag, present, and recommend their content to the user. refer comprises a set of powerful tools focusing on Named Entity Linking (NEL) which help authors and curators to semi-automatically analyze a platform’s textual content and semantically annotate it based on Linked Open Data. In refer automated NEL is complemented by manual semantic annotation supported by sophisticated autosuggestion of candidate entities, implemented as publicly available Wordpress plugin. In addition, refer visualizes the semantically enriched documents in a novel navigation interface for improved exploration of the entire content across the platform. The efficiency of the presented approach is supported by a qualitative evaluation of the user interfaces
- …