Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Testing implications of data dependencies
ACM Transactions on Database Systems (TODS)
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
The Implication Problem for Data Dependencies
Proceedings of the 8th Colloquium on Automata, Languages and Programming
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Data exchange: semantics and query answering
Theoretical Computer Science - Database theory
Machine Learning
OntoBayes: An Ontology-Driven Uncertainty Model
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Expressive probabilistic description logics
Artificial Intelligence
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
MayBMS: a probabilistic database management system
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Hybrid reasoning with rules and ontologies
Semantic techniques for the web
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Providing support for full relational algebra in probabilistic databases
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Probabilistic Databases
DIADEM: domain-centric, intelligent, automated data extraction methodology
Proceedings of the 21st international conference companion on World Wide Web
Query answering under probabilistic uncertainty in Datalog+ / - ontologies
Annals of Mathematics and Artificial Intelligence
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The recently introduced Datalog+/- family of ontology languages is especially useful for representing and reasoning over lightweight ontologies, and is set to play a central role in the context of query answering and information extraction for the Semantic Web. Recently, it has become apparent that it is necessary to develop a principled way to handle uncertainty in this domain. In addition to uncertainty as an inherent aspect of the Web, one must also deal with forms of uncertainty due to inconsistency and incompleteness, uncertainty resulting from automatically processing Web data, as well as uncertainty stemming from the integration of multiple heterogeneous data sources. In this paper, we take an important step in this direction by developing the first probabilistic extension of Datalog+/-. This extension uses Markov logic networks as underlying probabilistic semantics. Here, we especially focus on scalable algorithms for answering threshold queries, which correspond to the question "what is the set of all atoms that are inferred from a given probabilistic ontology with a probability of at least p?". These queries are especially relevant to Web information extraction, since uncertain rules lead to uncertain facts, and only information with a certain minimum confidence is desired.We present two algorithms: a basic approach and one based on heuristics that is guaranteed to return sound results.