Generalized vector space model
The Generalized vector space model is a generalization of the vector space model used in information retrieval. Wong "et al." presented an analysis of the problems that the pairwise orthogonality assumption of the vector space model (VSM) creates. From here they extended the VSM to the generalized vector space model (GVSM). Definitions. GVSM introduces term to term correlations, which deprecate the pairwise orthogonality assumption. More specifically, the factor considered a new space, where each term vector "t" was expressed as a linear combination of "2" vectors "m" where "r = 1...2". For a document "d  and a query q" the similarity function now becomes: where "t" and "t" are now vectors of a "2" dimensional space. Term correlation formula_2 can be implemented in several ways. For an example, Wong et al. uses the term occurrence frequency matrix obtained from automatic indexing as input to their algorithm. The term occurrence and the output is the term correlation between any pair of index terms. Semantic information on GVSM. There are at least two basic directions for embedding term to term relatedness, other than exact keyword matching, into a retrieval model: Recently Tsatsaronis focused on the first approach . They measure semantic relatedness ("SR") using a thesaurus ("O") like WordNet. It considers the path length, captured by compactness ("SCM"), and the path depth, captured by semantic path elaboration ("SPE"). They estimate the formula_2 inner product by: formula_4 where "s" and "s" are senses of terms "t" and "t" respectively, maximizing formula_5. Building also on the first approach, Waitelonis et al. have computed semantic relatedness from Linked Open Data resources including DBpedia as well as the YAGO taxonomy. Thereby they exploits taxonomic relationships among semantic entities in documents and queries after named entity linking.