{'fraction_non_alphanumeric': 0.017664376840039256, 'fraction_numerical': 0.0, 'mean_word_length': 5.144578313253012, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Fuzzy-Ontology Based Information Retrieval System for Relevant Feedback

Obtaining correct and relevant information at the right time to user’s query is quite a difficult task. This becomes even complex, if the query terms have many meanings and occur in different varieties of domain. This paper presents a fuzzy-ontology based information retrieval system that determine the semantic equivalence between terms in a query and terms in a document by relating the synonyms of query terms with those of document terms. Hence, documents could be retrieved based on the meaning of query terms. The challenge has been that surface form does not sufficiently retrieve relevant document to user’s query. However, the results presented showed that the Fuzzy-Ontology Information Retrieval system successfully retrieve relevant documents to user’s query. This is irrespective of different meaning and varieties of domain. The System was tested on words with different meanings and some set of user’s query from varied domains.