Abstract Attributes, whether qualitative or non-qualitative are the formal description of any real-world entity and are crucial in modern knowledge representation models like ontology. Though ample evidence for the amount of research done for mining non-qualitative attributes (like part-of relation) extraction from text as well as the Web is available in the wealth of literature, on the other side limited research can be found relating to qualitative attribute (i.e., size, color, taste etc.,) mining. Herein this research article an analytical framework has been proposed to retrieve qualitative attribute values from unstructured domain text. The research objective covers two aspects of information retrieval (1) acquiring quality values from unstructured text and (2) then assigning attribute to them by comparing the Google derived meaning or context of attributes as well as quality value (adjectives). The goal has been accomplished by using a framework which integrates Vector Space Modelling (VSM) with a probabilistic Multinomial Naive Bayes (MNB) classifier. Performance Evaluation has been carried out on two data sets (1) HeiPLAS Development Data set (106 adjective-noun exemplary phrases) and (2) a text data set in Medicinal Plant Domain (MPD). System is found to perform better with probabilistic approach compared to the existing pattern-based framework in the state of art.

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