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JASIS
2000

Probabilistic datalog: Implementing logical information retrieval for advanced applications

8 years 3 months ago
Probabilistic datalog: Implementing logical information retrieval for advanced applications
In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Datalog (function-free Horn clause predicate logic) with probability theory. Therefore, probabilistic weights may be attached to both facts and rules. The underlying semantics extends the well-founded semantics of modularly stratified Datalog to a possible worlds semantics. By using default independence assumptions with explicit specification of disjoint events, the inference process always yields point probabilities. We describe an evaluation method and present an implementation. This approach allows for easy formulation of specific retrieval models for arbitrary applications, and classical probabilistic IR models can be implemented by specifying the appropriate rules. In comparison to other approaches, the possibility of recursive rules allows for more powerful inferences, and predicate logic gives the express...
Norbert Fuhr
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2000
Where JASIS
Authors Norbert Fuhr
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