Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Jena: implementing the semantic web recommendations
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Expressive probabilistic description logics
Artificial Intelligence
PR-OWL: A Framework for Probabilistic Ontologies
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Query Evaluation on Probabilistic RDF Databases
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
SPARQL query answering with RDFS reasoning on correlated probabilistic data
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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Resource Description Framework (RDF) and its extension RDF Schema (RDFS) are data models to represent information on the Web. They use RDF triples to make statements. Because of lack of knowledge, some triples are known to be true with a certain degree of belief. Existing approaches either assign each triple a probability and assume that triples are statistically independent of each other, or only model statistical relationships over possible objects of a triple. In this paper, we introduce probabilistic RDFS (pRDFS) to model statistical relationships among correlated triples by specifying the joint probability distributions over them. Syntax and semantics of pRDFS are given. Since there may exist some truth value assignments for triples that violate the RDFS semantics, an algorithm to check the consistency is provided. Finally, we show how to find answers to queries in SPARQL. The probabilities of the answers are approximated using a Monte-Carlo algorithm.