Applied categorical data analysis
Applied categorical data analysis
Combining model-oriented and description-oriented approaches for probabilistic indexing
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic retrieval revisited
The Computer Journal - Special issue on information retrieval
Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of GIOSS for the text database discovery problem
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
A decision-theoretic approach to database selection in networked IR
ACM Transactions on Information Systems (TOIS)
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Evaluating different methods of estimating retrieval quality for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
Information Retrieval
Actions, answers, and uncertainty: a decision-making perspective on Web-based question answering
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Information Processing and Management: an International Journal
Score Distributions in Information Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
A signal-to-noise approach to score normalization
Proceedings of the 18th ACM conference on Information and knowledge management
Inferring document utility via a decision-making based retrieval model
International Journal of Knowledge-based and Intelligent Engineering Systems
Modeling information sources as integrals for effective and efficient source selection
Information Processing and Management: an International Journal
Modeling score distributions in information retrieval
Information Retrieval
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Uncertain inference is a probabilistic generalisation of the logical view on databases, ranking documents according to their probabilities that they logically imply the query. For tasks other than ad-hoc retrieval, estimates of the actual probability of relevance are required. In this paper, we investigate mapping functions between these two types of probability. For this purpose, we consider linear and logistic functions. The former have been proposed before, whereas we give a new theoretic justification for the latter. In a series of upper-bound experiments, we compare the goodness of fit of the two models. A second series of experiments investigates the effect on the resulting retrieval quality in the fusion step of distributed retrieval. These experiments show that good estimates of the actual probability of relevance can be achieved, and the logistic model outperforms the linear one. However, retrieval quality for distributed retrieval (only merging, without resource selection) is only slightly improved by using the logistic function.