Dirac Notation, Fock Space and Riemann Metric Tensor in Information Retrieval Models

Abstract

Using Dirac Notation as a powerful tool, we investigate the three classical Information Retrieval (IR) models and some their extensions. We show that almost all such models can be described by vectors in Occupation Number Representations (ONR) of Fock spaces with various specifications on, e.g., occupation number, inner product or term-term interactions. As important cases of study, Concept Fock Space (CFS) is introduced for Boolean model; the basic formulas for Singular Value Decomposition (SVD) of Latent Semantic Indexing (LSI) Model are manipulated in terms of Dirac notation. And, based on SVD, a Riemannian metric tensor is introduced, which not only can be used to calculate the relevance of documents to a query, but also may be used to measure the closeness of documents in data clustering.