Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
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On the consistency of defeasible databases
Artificial Intelligence
What does a conditional knowledge base entail?
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A taxonomy of complexity classes of functions
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Computing functions with parallel queries to NP
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Qualitative probabilities for default reasoning, belief revision, and causal modeling
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Nonmonotonic reasoning, conditional objects and possibility theory
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Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential
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Weak nonmonotonic probabilistic logics
Artificial Intelligence
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Reasoning within fuzzy description logics
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Abductive matchmaking using description logics
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Reducing OWL entailment to description logic satisfiability
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P-CLASSIC: a tractable probablistic description logic
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Expressive probabilistic description logics
Artificial Intelligence
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Database preference queries--a possibilistic logic approach with symbolic priorities
Annals of Mathematics and Artificial Intelligence
Rank prediction for semantically annotated resources
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We introduce conditional preference bases as a means for ranking objects in ontologies. Conditional preference bases consist of a description logic knowledge base and a finite set of conditional preferences, which are statements of the form "generally, in the context Φ, property α is preferred over property ¬α with strength s". They are inspired by variable-strength defaults in conditional knowledge bases. We define the notion of consistency for conditional preference bases, and we show how consistent conditional preference bases can be used for ranking objects in ontologies, where every object represents essentially a set of individuals that are sharing the same ranking-relevant properties. More concretely, we define two object rankings, denoted κsum and κlex, which evaluate the strengths of conditional preferences in an additive and a lexicographic way, respectively. Furthermore, we provide algorithms for the main computational tasks for ranking objects under conditional preference bases, we analyze the complexity of these tasks, and we delineate a tractable special case. To give evidence of the usefulness of this approach in practice, we describe two applications in the areas of product and literature search, where it allows especially for a flexible user-defined ranking of the query results reflecting personal preferences.