ARES: a relational database with the capability of performing flexible interpretation of queries
IEEE Transactions on Software Engineering
Retrieval of multimedia documents by imprecise query specification
EDBT '90 Proceedings of the 2nd international conference on extending database technology: Advances in Database Technology
A Knowledge-Based Approach For Database Flexible Querying
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
Query result ranking over e-commerce web databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Providing Flexible Queries over Web Databases
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
A Knowledge-Based Approach for Answering Fuzzy Queries over Relational Databases
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Improving web database search incorporating users query information
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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Users often have vague or imprecise ideas when searching the e-commerce Web databases such as used cars databases, houses databases etc. and may not be able to formulate queries that accurately express their query intentions. They also would like to obtain the relevant information that meets their needs and preferences closely. In this paper, we present a new approach --- QRR (query relaxation and ranking), for relaxing the initial query over e-commerce Web databases in order to provide relevant answer to the user. QRR relaxes the query criteria by adding the most similar values into each query criterion range specified by the initial query, and then the relevant answers which satisfy the relaxed queries could be retrieved. For relevant query results, QRR speculates the importance of each attribute based on the user initial query and assigns the score of each attribute value according to its "desirableness" to the user, and then the relevant answers are ranked according to their satisfaction degree to the user's needs and preferences. Experimental results demonstrate that QRR can effectively recommend the relevant information to the user and have a high ranking quality as well.