Probabilistic Datalog—a logic for powerful retrieval methods
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Model checking
The VLDB Journal — The International Journal on Very Large Data Bases
E-discovery revisited: the need for artificial intelligence beyond information retrieval
Artificial Intelligence and Law
A descriptive approach to classification
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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Many IR models and tasks rely on a mathematical specification, and, in order to check its correctness, extensive testing and manual inspection is usually carried out. However, a formal guarantee can be particularly difficult, or even impossible, to provide. This poster highlights the relationship between the mathematical specification of IR algorithms and their modelling, using a logic-based abstraction that minimises the gap between the specification and a concrete implementation. As a result, the semantics of the program are well-defined and correctness checks can be applied. This methodology is illustrated with the mathematical specification, and logic modelling of a Bayesian classifier with Laplace smoothing. In addition to closing the gap between specification and modelling, and the fact that checking the correctness of a model's implementation becomes an inherent part of the design process, this work can lead to the automatic translation between the mathematical definition and its modelling.