E L description logic modeling querying web and learning imperfect user preferences

Abstract

In this position paper we share ideas on modeling querying web resources by (imperfect) combination of particular user preferences based on description logic. Our basic assumption is, that web resources are modeled crisp. Imperfection (uncertainty, vagueness,...) comes from user context and preferences. We offer a model based on connection between three EL-description logic systems: classical, annotated(fuzzy) and a new variant of Bayesian description logic. The Bayesian part enables learning each single user's combination function and concepts.

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equivalent with an extension of classical logic programs with monotonicity axioms added, and these are (weakly) embeddable into Bayesian logic program of K. Kersting and L. De Raedt [ 4 ]. Experiments where provided with Bayesian networks BN (cheap, close and vacation are random variables over T ).

In this position paper we share ideas on using connections between classical E L description logic, f-E L@ description logic and a new variant of Bayesian description logic B-E L for modeling user preference querying web resources.

Several models of Bayesian description logic were described already in [ 1 ]. In [ 6 ] Bayesian description logic programs are described. All mentioned systems use a random distribution on the domain ΔI , or a measure on the power set of ΔI and provide a challenge of inducing probability distribution over web resources, properties and values.

Our system does not model randomness over web data. Uncertainty and vagueness comes with user and his/her context, interpretation of web data and preferences. Resource data (roles price, distance) remain crisp, randomness is touching only user query concepts (like a hotel for vacation) and user preference concepts (like cheap, close) which are random variable over the ordinal preference scale T . A cpd can describe user preference depending on distribution of ∃price.cheap and ∃distance.close.

We did not provide any experiments with this model. Our experiments in the logic programming framework give a well supported expectation that this could be a technically sound candidate for a simple, sufficiently expressive model for description of user preference querying of web resources. This leads to a layered model - web resources are crisp and each single user specific part with imperfection and learning ability.

References

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