DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding and approximating top-k answers in keyword proximity search
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Keyword search in databases: the power of RDBMS
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Effective Top-k Keyword Search in Relational Databases Considering Query Semantics
Advances in Web and Network Technologies, and Information Management
PerK: personalized keyword search in relational databases through preferences
Proceedings of the 13th International Conference on Extending Database Technology
Study on efficiency and effectiveness of KSORD
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
SPARK2: Top-k Keyword Query in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
iSearch: an interpretation based framework for keyword search in relational databases
KEYS '12 Proceedings of the Third International Workshop on Keyword Search on Structured Data
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Keyword search in relational databases allows the user to search information without knowing database schema and using structural query language. As results needed by user are assembled from connected tuples of multiple relations, ranking keyword queries are needed to retrieve relevant results. For a given keyword query, the authors first generate candidate networks and also produce connected tuple trees according to the generated candidate networks by reducing the size of intermediate joining results. They then model the generated connected tuple trees as a document and evaluate score for each document to estimate its relevance. Finally, the authors retrieve top-k keyword queries by ranking the results. In this paper, the authors propose a new ranking method based on virtual document. They also propose Top-k CTT algorithm by using the frequency threshold value. The experimental results are shown by comparison of the proposed ranking method and the previous ranking methods on IMDB and DBLP datasets.