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On the modelling of ranking algorithms in probabilistic datalog
2013
Proceedings of the 7th International Workshop on Ranking in Databases - DBRank '13
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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
doi:10.1145/2524828.2524832
dblp:conf/vldb/RoellekeBM13
fatcat:li56czzxsbcmna4kvbw24khvru