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Ontologies as Expectations of Term Co-occurrences

Vector space models (VSMs) are often employed as mathematical representations of documents for tasks like indexing, information retrieval (IR), filtering and others, where documents are represented as vectors of their index terms or keywords. In a vector space representation, interrelations between individual terms in the vector are not captured. IR tasks like classification or search rely on the similarity between such vectors and are limited by not being able to account for term dependencies. Past techniques have demonstrated the use of external information like dictionaries to enrich terms with semantic information. They do so by normalizing weights of terms in vectors by considering inter-term relationships like synonyms or class hierarchical relationships. Here, we present a generic term vector altering system that goes one step further to exploit the semantic relatedness between terms available in Ontologies. We demonstrate the value addition for the task of supervised document classification in the National Security domain. Results indicate promises in using such inter-term relationships, showing definite improvements in precision and recall of classifications and illustrate cases when the use of Ontologies is most beneficial.