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.
[1]
Hany Azzam,et al.
Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes
,
2007,
The VLDB Journal.
[2]
Thomas Roelleke,et al.
A Descriptive Approach to Classification
,
2011,
ICTIR.
[3]
Stephan Merz,et al.
Model Checking
,
2000
.
[4]
Norbert Fuhr,et al.
Probabilistic Datalog—a logic for powerful retrieval methods
,
1995,
SIGIR '95.
[5]
Jack G. Conrad.
E-Discovery revisited: the need for artificial intelligence beyond information retrieval
,
2010,
Artificial Intelligence and Law.