Effective semantic-based keyword search over relational databases for knowledge discovery

Keyword-based search has been popularized by Internet web search engines such as Google which is the most commonly used search engine to locate the information on the web. On the other hand while traditional database management systems offer powerful query languages such as SQL, they do not provide keyword-based search similar to the one provided by web search engines. The current amount of text data in relational databases is massive and is growing fast. This increases the importance and need for non-technical users to be able to search for such information using simple keyword search just as how they would search for text documents on the web. Keyword search over relational databases (KSRDBs) enables ordinary users to query relational databases by simply submitting keywords without having to know any SQL or having any knowledge of the underlying structure of the data. In this research work our primary focus is to enhance the effectiveness of the keyword search over relational databases using semantic web technologies. We have also addressed some the issues with the effectiveness of the current keyword search over relational databases. In particular we are addressing the followings: We have improved (gained significantly higher precision/recall curve) the existing state-of-the-art ranking functions by incorporating the query keywords\u27 proximity and query keywords\u27 quadgrams of the text attributes with long string into the scoring function. We have adapted a novel approach in making keyword search recommendations based on the text attributes in which the search terms were found without relying on the user\u27s past search criteria. A proof of concept (POC) prototype system called TupleRecommender has been implemented based on this approach. We have designed and implemented a proof of concept (POC) prototype system called database semantic search explorer (DBSemSXplorer) which can answer the traditional keyword search over relational databases in a more effective way with a better presentation of search results. This system is based on semantic web technologies and is equipped with faceted search and inference capability of the Semantic Web to ease the task of knowledge discovery for the end user