Classification and indexing of web content based on a model of semantic social bookmarking

One of the key challenges in Information Technology is finding a way to organize the knowledge present on the Web. This led to years of research on the integration of information, on the Semantic Web and related technologies. Information Search and Retrieval from the Web occur through a process of content disambiguation and search engines use algorithms and software agents in order to meet the needs of users and advertising buyers. Ex-post analytical agents and tools are becoming more pervasive, so much to cause increasing problems of privacy. Our work proposes an innovative approach of content disambiguation that overturns the ex-post semantic analysis of contents, because it deals with an ex-ante classification conducted on two axes: vertical one (hierarchical and taxonomic axis) and horizontal one (folksonomic axis through tags or keywords). This method, which is based on the logic of social bookmarking and focuses on semantic tagging, represents a new frontier in information architecture because it introduces a new way of classification made by people using keywords that have a specific lexical and semantic value. This approach will allow people to create a knowledge base of Web contents characterized by a precise semantic definition.