6533b7dafe1ef96bd126dda8
RESEARCH PRODUCT
Adding Knowledge Extracted by Association Rules into Similarity Queries
Monica Ribeiro Porto FerreiraMarcela Xavier RibeiroAgma TrainaRichard ChbeirCaetano Trainasubject
[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Webuser expectation[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceInformationSystems_DATABASEMANAGEMENTsimilarity queriescontent-based retrievalassociation rules[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][SCCO.COMP] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]SQL extensionquery rewriting[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]description
International audience; In this paper, we propose new techniques to improve the quality of similarity queries over image databases performing association rule mining over textual descriptions and automatically extracted features of the image content. Based on the knowledge mined, each query posed is rewritten in order to better meet the user expectations. We propose an extension of SQL aimed at exploring mining processes over complex data, generating association rules that extract semantic information from the textual description superimposed to the extracted features, thereafter using them to rewrite the queries. As a result, the system obtains results closer to the user expectation than it could using only the traditional, plain similarity query execution.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-01-01 |