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With the advent of the Internet, a new era of digital information exchangehas begun. Currently, the Internet encompasses more than five billion onlinesites and this number is exponentially increasing every day. Fundamentally,Information Retrieval (IR) is the science and practice of storing documents andretrieving information from within these documents. Mathematically, IR systemsare at the core based on a feature vector model coupled with a term weightingscheme that weights terms in a document according to their significance withrespect to the context in which they appear. Practically, Vector Space Model(VSM), Term Frequency (TF), and Inverse Term Frequency (IDF) are among otherlong-established techniques employed in mainstream IR systems. However, presentIR models only target generic-type text documents, in that, they do notconsider specific formats of files such as HTML web documents. This paperproposes a new semantic-sensitive web information retrieval model for HTMLdocuments. It consists of a vector model called SWVM and a weighting schemecalled BTF-IDF, particularly designed to support the indexing and retrieval ofHTML web documents. The chief advantage of the proposed model is that itassigns extra weights for terms that appear in certain pre-specified HTML tagsthat are correlated to the semantics of the document. Additionally, the modelis semantic-sensitive as it generates synonyms for every term being indexed andlater weights them appropriately to increase the likelihood of retrievingdocuments with similar context but different vocabulary terms. Experimentsconducted, revealed a momentous enhancement in the precision of web IR systemsand a radical increase in the number of relevant documents being retrieved. Asfurther research, the proposed model is to be upgraded so as to support theindexing and retrieval of web images in multimedia-rich web documents.

Semantic-Sensitive Web Information Retrieval Model for HTML Documents