On the modelling of ranking algorithms in probabilistic datalog

Thomas Roelleke, Marco Bonzanini, Miguel Martinez-Alvarez
2013 Proceedings of the 7th International Workshop on Ranking in Databases - DBRank '13  
TF-IDF, BM25, language modelling (LM), and divergence-fromrandomness (DFR) are popular ranking models. Providing logical abstraction for information search is important, but the implementation of ranking algorithms in logical abstraction layers such as probabilistic Datalog leads to many challenges regarding expressiveness and scalability. Though the ranking algorithms have probabilistic roots, the ranking score often is not probabilistic, leading to unsafe programs from a probabilistic point
more » ... view. In this paper, we describe the evolution of probabilistic Datalog to provide concepts required for modelling ranking algorithms.
doi:10.1145/2524828.2524832 dblp:conf/vldb/RoellekeBM13 fatcat:li56czzxsbcmna4kvbw24khvru