Enriching Domain Concepts with Qualitative
Attributes: A Text Mining based Approach
Niyati Kumari Behera and Guruvayur
Suryanarayanan Mahalakshmi
Department of Computer Science and Engineering, Anna
University, India
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.
Keywords: Information retrieval, text mining,
qualitative attribute, adjectives, natural language processing.
Received
July 24, 2019; accepted May 4, 2020