Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic routing and retrieval using Smart: TREC-2
TREC-2 Proceedings of the second conference on Text retrieval conference
TREC and TIPSTER experiments with INQUERY
TREC-2 Proceedings of the second conference on Text retrieval conference
Information Processing and Management: an International Journal - Special issue: history of information science
Evaluating the location of hot spots in interactive scenes using the 3R toolbox
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
RELIEF: combining expressiveness and rapidity into a single system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
ICCS '96 Proceedings of the 4th International Conference on Conceptual Structures: Knowledge Representation as Interlingua
Modelling multimodal interaction: A theory-based technique for design analysis support
INTERACT '97 Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction
Using Conceptual Graphs in a Multifaceted Logical Model for Information Retrieval
DEXA '96 Proceedings of the 7th International Conference on Database and Expert Systems Applications
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
A logical relational approach for information retrieval indexing
IRSG'97 Proceedings of the 19th Annual BCS-IRSG conference on Information Retrieval Research
Toward a User-Centered Image Retrieval System
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
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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.