Semantic-Sensitive Web Information Retrieval Model for HTML Documents

With the advent of the Internet, a new era of digital information exchange has begun. Currently, the Internet encompasses more than five billion online sites and this number is exponentially increasing every day. Fundamentally, Information Retrieval (IR) is the science and practice of storing documents and retrieving information from within these documents. Mathematically, IR systems are at the core based on a feature vector model coupled with a term weighting scheme that weights terms in a document according to their significance with respect to the context in which they appear. Practically, Vector Space Model (VSM), Term Frequency (TF), and Inverse Term Frequency (IDF) are among other long-established techniques employed in mainstream IR systems. However, present IR models only target generic-type text documents, in that, they do not consider specific formats of files such as HTML web documents. This paper proposes a new semanticsensitive web information retrieval model for HTML documents. It consists of a vector model called SWVM and a weighting scheme called BTF-IDF, particularly designed to support the indexing and retrieval of HTML web documents. The chief advantage of the proposed model is that it assigns extra weights for terms that appear in certain pre-specified HTML tags that are correlated to the semantics of the document. Additionally, the model is semantic-sensitive as it generates synonyms for every term being indexed and later weights them appropriately to increase the likelihood of retrieving documents with similar context but different vocabulary terms. Experiments conducted, revealed a momentous enhancement in the precision of web IR systems and a radical increase in the number of relevant documents being retrieved. As further research, the proposed model is to be upgraded so as to support the indexing and retrieval of web images in multimedia-rich web documents.