Probabilistic Datalog - a Logic for Powerful Retrieval Methods (1995)
| Venue: | Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Citations: | 60 - 18 self |
BibTeX
@INPROCEEDINGS{Fuhr95probabilisticdatalog,
author = {Norbert Fuhr},
title = {Probabilistic Datalog - a Logic for Powerful Retrieval Methods},
booktitle = {Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
year = {1995},
pages = {282--290},
publisher = {ACM}
}
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Abstract
ing with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or <permissions@acm.org> 2 \Delta ---Since Datalog P allows for recursive rules, it provides more powerful inference than any other (implemented) probabilistic IR model. ---Finally, since Datalog P is a generalization of (deterministic) Datalog, it can be used as a standard query language for both IR and database systems, and thus also for integration of these two types of systems on the logical level. 2. INFORMAL DESCRIPTION OF Datalog P Probabilistic Datalog is an extension of stratified Datalog (see e.g. [Ullman 88], [Ceri et al. 90]). On the syntactical level, the only difference is that with ground facts, also a probabilistic weight may be given, e.g. 0.7 indterm(d1,ir). 0.8 indterm(d1,db). Informally speaking, the probabilistic weight gives...
