Adaptive Concept Vector Space Representation Using Markov Chain Model

Abstract

This paper proposes an adaptive document representation (concept vector space model) using Markov Chain model. The vector space representation is one of the most common models for representing documents in classification process. The document classification based on ontology classification approach is represented as a vector, whose components are ontology concepts and their relevance. The relevance is represented the by frequency of concepts’ occurrence. These concepts make various contributions in classification process. The contributions depend on the position of concepts where they are depicted in the ontology hierarchy. The hierarchy such as classes, subclasses and instances may have different values to represent the concepts’ importance. The weights to define concepts’ importance are generally selected by empirical analysis and are usually kept fixed. Thus, making it less effective and time consuming. We therefore propose a new model to automatically estimate weights of concepts within the ontology. This model initially maps the ontology to a Markov chain model and then calculates the transition probability matrix for this Markov chain. Further, the transition probability matrix is used to compute the probability of steady states based on left eigenvectors. Finally, the importance is calculated for each ontology concept. And, an enhanced concept vector space representation is created with concepts’ importance and concepts’ relevance. The concept vector space representation can be adapted for new ontology concepts.