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the real world, however, most spatial objects are associated with textual information and lie on road networks. In this paper, we introduce a new type of queries, namely, reverse top-k Boolean spatial keyword (RkBSK) retrieval, which assumes objects are on the road
. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree
Efficient discovery of information based on partially specified and misspelled query keywords is a challenging problem in large scale peer-to-peer (P2P) networks. This paper presents QPM, a P2P search mechanism for efficient information retrieval with misspelled and partial keywords. QPM uses the double metaphone
We introduce a new method for discovering latent topics in sets of objects, such as documents. Our method, which we call PARIS (for Principal Atoms Recognition In Sets), aims to detect principal sets of elements, representing latent topics in the data, that tend to appear frequently together. These latent topics, which we refer to as `atoms', are used as the basis for clustering, classification, collaborative...
include visual concepts together with linguistic keywords, are therefore needed to support video annotation up to the level of detail of pattern specification. This paper presents pictorially enriched ontologies and provides a solution for their implementation in the soccer video domain. The pictorially enriched ontology is
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