An Enhanced WordNet Query Expansion Approach for Ontology Based Information Retrieval System

Abstract

Ontology-based information retrieval is described as a cutting-edge approach capable to enhance the returns of semantic results from documents. This approach works better when similar and relevant terms are added to user’s initial query terms using data sources such as wordnet; such technique is known as query expansion. However, the precision of the added term(s) tends to be inaccurate because of the existing WordNet’s deficit to handle inflected forms of words. In lieu of this development, this research aims to design Rule based Web Ontology Language (OWL) Information Retrieval System with an enhanced wordnet for query expansion but only limited to the noun subnet database. A combined ontology development methodology was implored; and OWL-2 to develop the ontology for a novel domain of maize crop considering primarily soil, fertilizer and irrigation knowledge. Its rule-based ontology because Competency Questions were modeled using First-Order-Logic (FOL) and encoded with Semantic Web Rule Language (SWRL). Similarly, the wordnet was enhanced on python environment considering the lemmatization’s lookup table and the third party modules of Natural Language Tool Kits (NLTK), pattern.en and enchant. Therefore, in this research, the improved wordnet can handle inflected word without stemming it to the root word. It also correctly suggested related words in the case of user’s wrong spelt word thereby; reduces minimally time wastage and fatigue. This development invariably aids ontology validation along with the other forms of validations carried out. The research ultimately offers an effective ontology-based information retrieval system based on the proposed algorithmic framework.