Expanding Fuzzy Search in Relational databases using Ontology

Sumi Maria Abraham, Sulphikar A


Web search engines have popularized the concept of keyword search (KWS). Database KWS identifies the data in the database which is related to user provided keywords and to identify the hidden relationships between them. Users may not be able to provide the exact keywords which are required to retrieve the required information. In this paper we propose an indexing mechanism to identify the location of keywords in a user query. Each index entry denotes the base form of a term along with the list of tables and rows which contain that term. The index is stored in a NoSQL database. Fuzzy search is used to find keywordswhich are similar to the keyword entered by the user and ontology data model is used to expand or revise the keywords provided by the user. When a user submits a query, the rows containing the keywords or related keywords are identified. The retrieved rows are ranked based on similarity between query keywords and retrieved keywords, and the number of keywords it contain. If the result set is empty, WordNet ontology will be used to relax the query by expanding the query keywords. The related keywords discovered are also stored in the index so that future searches based on them can be made faster

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