Abstract
In information retrieval systems, it is common practice to rank the retrieved documents in decreasing order of their estimated relevance to the user’s query. Information retrieval models, such as the vector-space model (see Salton’s work), provide weighting schemes and matching functions that follow this necessity. However, they were mainly developed in the context of textual document retrieval. The contribution of this paper is twofold. Firstly, it takes a look at the challenges involved in the ordering of the results in image retrieval, while using the expressive conceptual graphs formalism as the indexing language. New parameters appear to be useful in the vector-space weighting schemes, that take into account the richness and complexity of documents such as images. We inspect such parameters and give a flexible weighting scheme. Secondly, this paper gives a general weighting scheme, applied for the conceptual graphs formalism. The matching function of this formalism, which otherwise gives only a boolean yes or no decision on a document’s relevance to a user’s query, is refined so that to obtain ranked results.
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Ounis, I. (1999). A Flexible Weighting Scheme for Multimedia Documents. In: Bench-Capon, T.J., Soda, G., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1999. Lecture Notes in Computer Science, vol 1677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48309-8_37
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DOI: https://doi.org/10.1007/3-540-48309-8_37
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