AUTOMATIC SEMANTIC WEB ANNOTATION BY APPLYING ASSOCIATIVE CONCEPT CLASSIFIER IN TEXT
Behnam Hajian, Kamran Zamanifar · 2009
After appearance of semantic web, the framework which is machine-readable and machine-understandable, by Berners Lee, current web should be annotated by W3C standards in order to define semantic domain of each word by its ontology to alleviate the posed problems in the realm of search and information retrieval. However annotation is one major problem in the semantic web domain, which is presently performed by a human agent. But, considering low human precision for this time-consuming and expensive task and the advent of data mining in recent years, in this article, a system is proposed for automatic semantic web annotation that is based on machine learning techniques for mining association rules between words in already annotated texts. In the present article, ASWAACC annotation system will be introduced. In this system, annotation rules are produced by analysis of words co-occurrence in a paragraph to eliminate sense ambiguity of words. This system has two phases: 1-learning by mining association rules in text data which has been already annotated by human. 2-utilizing mined rules to automatically annotate the new unseen text data in order to define domain ontology of each word.