The definition of term depends on the application. Typically terms are single words, keywords, or longer phrases. If the words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary A document is represented as a vector. Each dimensions corresponds to a separate terms. If a term occurs in the document, its value in the vector is non-zero.
Relevancy rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as same kind of vector as the documents.
LIMITATION:
There is some limitation of vector space model.
Models based on and extending the vector space model include:
•	Generalized vector space model.
•	(enhanced) Topic-based Vector Space Model [1] (eTVSM) — Extends the vector space model by removing the constraint that the term-vectors be orthogonal. In contrast to the generalized vector space model the (enhanced) Topic-based Vector Space Model does not depend on concurrence-based similarities between terms. The enhancement of the enhanced Topic-based Vector Space Model  (compared to the not enhanced one) is a proposal on how to derive term-vectors from an Ontology.