Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Framework for Representing Knowledge
A Framework for Representing Knowledge
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Machine Learning
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
The Description Logic Handbook
The Description Logic Handbook
Yago: a core of semantic knowledge
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Autonomously semantifying wikipedia
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SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on World wide web
Probabilistic databases: diamonds in the dirt
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
NAGA: Searching and Ranking Knowledge
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
STAR: Steiner-Tree Approximation in Relationship Graphs
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
MING: mining informative entity relationship subgraphs
Proceedings of the 18th ACM conference on Information and knowledge management
Corroborating information from disagreeing views
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Web Semantics: Science, Services and Agents on the World Wide Web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Active knowledge: dynamically enriching RDF knowledge bases by web services
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The Journal of Machine Learning Research
Exploring different types of trust propagation
iTrust'06 Proceedings of the 4th international conference on Trust Management
Tutorial on statistical relational learning
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise
Proceedings of the fourth ACM international conference on Web search and data mining
Database researchers: plumbers or thinkers?
Proceedings of the 14th International Conference on Extending Database Technology
Crowd IQ: aggregating opinions to boost performance
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Crowd IQ: measuring the intelligence of crowdsourcing platforms
Proceedings of the 3rd Annual ACM Web Science Conference
Human-machine cooperation with epistemological DBs: supporting user corrections to knowledge bases
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
Reasoning about knowledge from the web
ICWE'12 Proceedings of the 12th international conference on Current Trends in Web Engineering
An analysis of human factors and label accuracy in crowdsourcing relevance judgments
Information Retrieval
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Current knowledge bases suffer from either low coverage or low accuracy. The underlying hypothesis of this work is that user feedback can greatly improve the quality of automatically extracted knowledge bases. The feedback could help quantify the uncertainty associated with the stored statements and would enable mechanisms for searching, ranking and reasoning at entity-relationship level. Most importantly, a principled model for exploiting user feedback to learn the truth values of statements in the knowledge base would be a major step forward in addressing the issue of knowledge base curation. We present a family of probabilistic graphical models that builds on user feedback and logical inference rules derived from the popular Semantic-Web formalism of RDFS [1]. Through internal inference and belief propagation, these models can learn both, the truth values of the statements in the knowledge base and the reliabilities of the users who give feedback. We demonstrate the viability of our approach in extensive experiments on real-world datasets, with feedback collected from Amazon Mechanical Turk.