DBSemSXplorer: semantic-based keyword search system over relational databases for knowledge discovery

Keyword search over relational databases has been broadly studied in recent years. Research works have been done to address both the efficiency and the effectiveness of the keyword search over relational databases. One issue with keyword search in general is its ambiguity which can ultimately impact the effectiveness of the search in terms of the quality of the search results. This ambiguity is primarily due to the ambiguity of the contextual meaning of each term in the query (e.g. each query term can be mapped to different schema terms with the same name or their synonyms). In addition to the query ambiguity itself, the relationships between the keywords in the search results are crucial for the proper interpretation of the search results by the user and should be clearly presented in the search results. To address these issues we have designed and implemented a prototype system DBSemSXplorer which can answer the traditional keyword search over relational databases in a more effective way with a better presentation of search results. We address the keyword search ambiguity issue by adapting some of the existing approaches for keyword mapping from the query terms to the schema terms/instances. The approaches we have adapted for term mapping capture both the syntactic similarity between the query keywords and the schema terms as well as the semantic similarity (e.g. definition of the keywords) of the two and give better mappings and ultimately more accurate results. Finally, to address the last issue of lacking clear relationships among the terms appearing in the search results, our system has leveraged semantic web technologies in order to enrich the knowledgebase and to discover the relationships between the keywords. Our experiments show that our system is more effective than the traditional keyword search approaches by enabling the users to find the search results which are more relevant to their keyword queries.