26,254 research outputs found
Multi-View Learning and Link Farm Discovery
The first part of this abstract focuses on estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Mixture model estimation is a key problem for both semi-supervised and unsupervised learning. An appropriate optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. An EM algorithm maximizes this criterion. The second part of this abstract focuses on the problem of identifying link spam. Search engine optimizers inflate the page rank of a target site by spinning an artificial web for the sole purpose of providing inbound links to the target. Discriminating natural from artificial web sites is a difficult multi-view problem
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
- …